Exploratory Text Analytics Final Project

• Arti Patel ()
• Sudeepti Surapaneni(ss9ud@virginia.edu)
• Adonis Lu ()
• DS 5001
• 29 April 2020

Deliverables 1) A collection of source files compressed in an archive (e.g., zip or tar.gz) and hosted on your UVA Box account. A manifest file describing those sources files, including their: Provenance: Where did they come from? Describe the website or other source and provide relevant URLs. Location: Provide a link to the source files in UVA Box. Description: What is the general subject matter of the corpus? Format: A description of both the file formats of the source files, e.g., plaintext, XML, CSV, etc., and the internal structure where applicable. For example, if XML then specify document type (e.g., TEI or XHTML).

Import

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
%matplotlib inline
from glob import glob
import re
import nltk
import plotly_express as px

# testing with one file first epub_file = "ALCOTT_MAY_LOUISA_AN_OLD-FASHIONED_GIRL-pg2787.txt" epub_file2 = "ALCOTT_MAY_LOUISA_EIGHT_COUSINS-pg2726.txt" epub_file3 = "ALCOTT_MAY_LOUISA_JACK_AND_JILL-pg2786.txt" epub_file4 = "ALCOTT_MAY_LOUISA_LITTLE_WOMEN-pg514.txt" csv_file1 = 'alcott_ofg.csv' csv_file2 = 'alcott_8cos.csv' csv_file3 = 'alcott_jj.csv' csv_file4 = 'alcott_lw.csv'OHCO = ['chap_num', 'para_num', 'sent_num', 'token_num']import os os.getcwd()

Import file into a dataframe

#epubs = '/Users/su/Desktop/Text_Analytics_Final/epubs' epub = open(epub_file, 'r', encoding='utf-8-sig').readlines() epub2 = open(epub_file2, 'r', encoding='utf-8-sig').readlines() epub3 = open(epub_file3, 'r', encoding='utf-8-sig').readlines() epub4 = open(epub_file4, 'r', encoding='utf-8-sig').readlines()df = pd.DataFrame(epub, columns=['line_str']) df2 = pd.DataFrame(epub2, columns=['line_str']) df3 = pd.DataFrame(epub3, columns=['line_str']) df4 = pd.DataFrame(epub4, columns=['line_str']) df.index.name = 'line_num' df2.index.name = 'line_num' df3.index.name = 'line_num' df4.index.name = 'line_num' df.line_str = df.line_str.str.strip() df2.line_str = df2.line_str.str.strip() df3.line_str = df3.line_str.str.strip() df4.line_str = df4.line_str.str.strip() df.sample(10) df2.sample(10) df3.sample(10) df4.sample(10)

Extract title of work from first line

title = df.loc[0].line_str.replace('The Project Gutenberg EBook of ', '')title2 = df2.loc[0].line_str.replace('The Project Gutenberg EBook of ', '')title3 = df3.loc[0].line_str.replace('The Project Gutenberg EBook of ', '')title4 = df4.loc[0].line_str.replace('The Project Gutenberg EBook of ', '')titletitle2title3title4df['title'] = titledf2['title'] = title2df3['title'] = title3df4['title'] = title4df.head()df2.head()df3.head()df4.head()# Remove Gutenberg's front and back mattera = df.line_str.str.match(r"\*\*\*\s*START OF (THE|THIS) PROJECT") b = df.line_str.str.match(r"\*\*\*\s*END OF (THE|THIS) PROJECT") a2 = df2.line_str.str.match(r"\*\*\*\s*START OF (THE|THIS) PROJECT") b2 = df2.line_str.str.match(r"\*\*\*\s*END OF (THE|THIS) PROJECT") a3 = df3.line_str.str.match(r"\*\*\*\s*START OF (THE|THIS) PROJECT") b3 = df3.line_str.str.match(r"\*\*\*\s*END OF (THE|THIS) PROJECT") a4 = df4.line_str.str.match(r"\*\*\*\s*START OF (THE|THIS) PROJECT") b4 = df4.line_str.str.match(r"\*\*\*\s*END OF (THE|THIS) PROJECT")an = df.loc[a].index[0] bn = df.loc[b].index[0] an2 = df2.loc[a2].index[0] bn2 = df2.loc[b2].index[0] an3 = df3.loc[a3].index[0] bn3 = df3.loc[b3].index[0] an4 = df4.loc[a4].index[0] bn4 = df4.loc[b4].index[0] an, bndf.loc[an].line_strdf2.loc[an2].line_str df3.loc[an3].line_str df4.loc[an4].line_strdf.loc[bn].line_str df2.loc[bn2].line_str df3.loc[bn3].line_str df4.loc[bn4].line_str## Take a slice between a and bdf = df.loc[an + 1 : bn - 1]df2 = df2.loc[an2 + 1 : bn2 - 1] df3 = df3.loc[an3 + 1 : bn3 - 1] df4 = df4.loc[an4 + 1 : bn4 - 1]df.head()df.tail()# Chunk by chapter## Find all chapter headerschap_lines = df.line_str.str.match(r"^\s*(Chapter|CHAPTER)\s+(\d+)", case=False) chap_lines2 = df2.line_str.str.match(r"^\s*(Chapter|CHAPTER)\s+(\d+)", case=False) chap_lines3 = df3.line_str.str.match(r"^\s*(Chapter|CHAPTER)\s+(\d+)", case=False) chap_lines4 = df4.line_str.str.match(r"^\s*(Chapter|CHAPTER)\s+(\d+)", case=False)df.loc[chap_lines] df2.loc[chap_lines2] df3.loc[chap_lines3] df4.loc[chap_lines4]## Assign numbers to chaptersdf.loc[chap_lines, 'chap_num'] = [i+1 for i in range(df.loc[chap_lines].shape[0])] df2.loc[chap_lines2, 'chap_num'] = [i+1 for i in range(df2.loc[chap_lines2].shape[0])] df3.loc[chap_lines3, 'chap_num'] = [i+1 for i in range(df3.loc[chap_lines3].shape[0])] df4.loc[chap_lines4, 'chap_num'] = [i+1 for i in range(df4.loc[chap_lines4].shape[0])]df.loc[chap_lines] df2.loc[chap_lines2] df3.loc[chap_lines3] df4.loc[chap_lines4]df3.sample(4)## Forward-fill chapter numbers to following text linesdf.chap_num = df.chap_num.ffill()df.sample(10)## Clean updf = df.loc[~df.chap_num.isna()] # Remove everything before Chapter 1df = df.loc[~chap_lines] # Remove chapter heading linesdf.chap_num = df.chap_num.astype('int') # Convert chap_num from float to intdf.head(10)## Group lines by chapter num OHCO[:1]dfc = df.groupby(OHCO[:1]).line_str.apply(lambda x: '\n'.join(x)).to_frame() # Make big string for each chapterdfc.head()# Split chapters into paragraphs dfp = dfc['line_str'].str.split(r'\n\n+', expand=True).stack()\ .to_frame().rename(columns={0:'para_str'})# dfc['line_str'].str.split(r'\n\n+', expand=True)# dfc['line_str'].str.split(r'\n\n+', expand=True).stack()dfp.head()dfp.index.names = OHCO[:2]dfp.head()dfp['para_str'] = dfp['para_str'].str.replace(r'\n', ' ').str.strip() # Remove newlinesdfp = dfp[~dfp['para_str'].str.match(r'^\s*$')] # Remove empty paragraphsdfp.head()# Split paragraphs into sentences NOTE: ADDED `"` to regex in `split()`dfs = dfp['para_str'].str.split(r'[.?!;:"]+', expand=True).stack()\ .to_frame().rename(columns={0:'sent_str'})dfs.index.names = OHCO[:3]dfs = dfs[~dfs['sent_str'].str.match(r'^\s*$')] # Remove empty paragraphsdfs.head(10)# Split sentences into tokensdft = dfs['sent_str'].str.split(r"[\s',-]+", expand=True).stack()\ .to_frame().rename(columns={0:'token_str'})dft.index.names = OHCO[:4]dft.head(10)# Gathering by Content Objectsents = dft.groupby(OHCO[:3]).token_str.apply(lambda x: ' '.join(x)).to_frame().rename(columns={'token_str':'content'}) paras = dft.groupby(OHCO[:2]).token_str.apply(lambda x: ' '.join(x)).to_frame().rename(columns={'token_str':'content'}) chaps = dft.groupby(OHCO[:1]).token_str.apply(lambda x: ' '.join(x)).to_frame().rename(columns={'token_str':'content'})def gather(ohco_level): return df.groupby(OHCO[:ohco_level]).token_str\ .apply(lambda x: ' '.join(x))\ .to_frame()\ .rename(columns={'token_str':'content'})sents.sample(10)# Visualize some things## Token lengthdft.token_str.str.len().plot.hist(bins=10, title="Tokens");## Sentence lengthsents.content.str.len().plot.hist(bins=20, title='Sentences');## Paragraph lengthparas.content.str.len().plot.hist(title="Paragraphs");## Chapter lengthchaps.content.str.len().plot.hist(title="Chapters");# Save work to CSVdft.to_csv(csv_file)OHCO = ['book_id', 'chap_num', 'para_num', 'sent_num', 'token_num'] text_file1 = 'austen-persuasion.csv' text_file2 = 'austen-sense.csv'text1 = pd.read_csv(text_file1) text2 = pd.read_csv(text_file2)text1['script_id'] = 1 text2['script_id'] = 2tokens = pd.concat([text1, text2]).dropna()tokens = tokens.set_index(OHCO)tokens.head()tokens['term_str'] = tokens['token_str'].str.lower().str.replace(r'[\W_]', '')# Create a vocabularytokens.head()vocab = tokens['term_str'].value_counts()\ .to_frame()\ .reset_index()\ .rename(columns={'term_str':'n', 'index':'term_str'})\ .sort_values('term_str') vocab.index.name = 'term_id'vocab.head()vocab.sample(5)Specifically, you should produce the following tables as data frames and save them as CSV tables: A library table (LIBRARY) with basic metadata about each book. The raw book title will be sufficient, i.e. with title and author combined. The path of the source file. A document table (DOC) with the preserved paragraphs of each book and an appropriate OHCO index. A token table (TOKEN) with an appropriate OHCO index the following annotations derived from NLTK: Part-of-speech tags. A vocabulary (VOCAB) table of terms with the following annotations derived from NLTK: Stopwords. Porter stems.

# CONFIG 

OHCO = ['book_id', 'chap_num', 'para_num', 'sent_num', 'token_num']
epub_dir = 'epubs_'

Inspect

Since Project Gutenberg texts vary widely in their markup, we define our chunking patterns by hand.

roman = '[IVXLCM]+' caps = "[A-Z';, -]+" chap_pats = { 507: { 'start_line': 20, 'end_line': 16261, 'volume': re.compile('^\sVOLUME\s+{}\s$'.format(roman)), 'chapter': re.compile('^Chapter\s+{}'.format(roman)) }, 6688: { 'start_line': 22, 'end_line': 2556, 'chapter': re.compile('^Chapter\s+{}.*'.format(roman)) }, 145: { 'start_line': 205, 'end_line': 33310, 'chapter': re.compile('^CHAPTER\s+{}'.format(roman)) } }

roman = '[IVXLCM]+'
caps = "[A-Z';, -]+"
chap_pats = {
   

    2787: {
        'start_line': 21,
        'end_line': 11235,
        #'chapter': re.compile("^\s*CHAPTER\s+{}\.\s*$".format(roman))
        'chapter': re.compile("^\s*CHAPTER\s+{}\. .*$".format(roman))
    },
    
    
     2726: {
        'start_line': 21,
        'end_line': 11235,
        #'chapter': re.compile("^\s*CHAPTER\s+{}\.\s*$".format(roman))
        'chapter': re.compile("^Chapter\s+\d+.+$")
    },
    
      2786: {
        'start_line': 21,
        'end_line': 11235,
        'chapter': re.compile("^\s*Chapter\s+{}\. .*$".format(roman))
    },
    
    514: {
        'start_line': 21,
        'end_line': 11235,
        'chapter': re.compile('^CHAPTER+\s*{}\s*$'.format(caps))
    },
    
    
    3499: {
        'start_line': 21,
        'end_line': 10256,
        'chapter': re.compile("^Chapter\s+\d+.+$")
        
    },
    
    
     2788: {
        'start_line': 21,
        'end_line': 11620,
        #'chapter': re.compile('^CHAPTER+\s*{}\s*$'.format(roman))
         'chapter': re.compile("^\s*CHAPTER\s+{}\. .*$".format(roman))
    },
    
    
      2804: {
        'start_line': 21,
        'end_line': 11235,
        'chapter': re.compile("^Chapter\s+\d+.+$")
    },
    
    
     3795: {
        'start_line': 21,
        'end_line': 11235,
        'chapter': re.compile('^CHAPTER+\s*{}\s*$'.format(roman))
    }
 
}
    

Register and Chunk

def acquire_epubs(epub_list, chap_pats, OHCO=OHCO):
    
    my_lib = []
    my_doc = []

    for epub_file in epub_list:
        
        # Get PG ID from filename
        book_id = int(epub_file.split('-')[-1].split('.')[0].replace('pg',''))
        print("BOOK ID", book_id)
        
        # Import file as lines
        lines = open(epub_file, 'r', encoding='utf-8-sig').readlines()
        df = pd.DataFrame(lines, columns=['line_str'])
        df.index.name = 'line_num'
        df.line_str = df.line_str.str.strip()
        df['book_id'] = book_id
        
        # FIX CHARACTERS TO IMPROVE TOKENIZATION
        df.line_str = df.line_str.str.replace('—', ' — ')
        df.line_str = df.line_str.str.replace('-', ' - ')
        
        # Get book title and put into LIB table -- note problems, though
        
        book_title = re.sub(r"The Project Gutenberg eBook( of|,) ", "", df.loc[0].line_str, flags=re.IGNORECASE)
        #book_title = re.sub(r"The Project Gutenberg eBook( of|,) ", "", df.loc[1].line_str, flags=re.IGNORECASE)
        book_title = re.sub(r"Project Gutenberg's ", "", book_title, flags=re.IGNORECASE)
        
        
        # Remove cruft
        a = chap_pats[book_id]['start_line'] - 1
        b = chap_pats[book_id]['end_line'] + 1
        df = df.iloc[a:b]
        
        # Chunk by chapter
        chap_lines = df.line_str.str.match(chap_pats[book_id]['chapter'])
        chap_nums = [i+1 for i in range(df.loc[chap_lines].shape[0])]
        df.loc[chap_lines, 'chap_num'] = chap_nums
        df.chap_num = df.chap_num.ffill()

        # Clean up
        df = df[~df.chap_num.isna()] # Remove chapter heading lines
        df = df.loc[~chap_lines] # Remove everything before Chapter 1
        df['chap_num'] = df['chap_num'].astype('int')
        
        # Group -- Note that we exclude the book level in the OHCO at this point
        df = df.groupby(OHCO[1:2]).line_str.apply(lambda x: '\n'.join(x)).to_frame() # Make big string
        
        # Split into paragrpahs
        df = df['line_str'].str.split(r'\n\n+', expand=True).stack().to_frame().rename(columns={0:'para_str'})
        df.index.names = OHCO[1:3] # MAY NOT BE NECESSARY UNTIL THE END
        df['para_str'] = df['para_str'].str.replace(r'\n', ' ').str.strip()
        df = df[~df['para_str'].str.match(r'^\s*$')] # Remove empty paragraphs
        
        # Set index
        df['book_id'] = book_id
        df = df.reset_index().set_index(OHCO[:3])

        # Register
        my_lib.append((book_id, book_title, epub_file))
        my_doc.append(df)

    docs = pd.concat(my_doc)
    library = pd.DataFrame(my_lib, columns=['book_id', 'book_title', 'book_file']).set_index('book_id')
    print("Done.")
    return library, docs

epubs = [epub for epub in sorted(glob(epub_dir+'/*.txt'))]
LIB, DOC = acquire_epubs(epubs, chap_pats)

LIB_arti = pd.read_csv('LIB_arti.csv')
DOC_arti = pd.read_csv('DOC_arti.csv')

DOC_arti = DOC_arti.rename(columns = {'story_num':'chap_num'})
DOC_arti = DOC_arti.set_index(['book_id','chap_num','para_num'])

LIB['book_id'] = LIB.index

LIB = LIB.append(LIB_arti)
DOC = DOC.append(DOC_arti)

LIB.index = LIB['book_id']

BOOK ID 2787
BOOK ID 2726
BOOK ID 2786
BOOK ID 3499
BOOK ID 2788
BOOK ID 514
BOOK ID 2804
BOOK ID 3795
Done.

LIB

	book_title	book_file	book_id
book_id
2787	An Old - fashioned Girl, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_AN_OLD-FASHIONED_GIRL...	2787
2726	Eight Cousins, by Louisa M. Alcott	epubs_\ALCOTT_MAY_LOUISA_EIGHT_COUSINS-pg2726.txt	2726
2786	Jack and Jill, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JACK_AND_JILL-pg2786.txt	2786
3499	Jo's Boys, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JO’S_BOYS-pg3499.txt	3499
2788	Little Men, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_MEN-pg2788.txt	2788
514	Little Women, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_WOMEN-pg514.txt	514
2804	Rose in Bloom, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_ROSE_IN_BLOOM-pg2804.txt	2804
3795	Under the Lilacs, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_UNDER_THE_LILACS-pg37...	3795
1	The Works of Edgar Allan Poe Volume 1 (of 5) o...	epubs/2147-0.txt	1
2	The Works of Edgar Allan Poe Volume 2 (of 5) o...	epubs/2148-0.txt	2
3	The Works of Edgar Allan Poe Volume 3 (of 5) o...	epubs/2149-0.txt	3
4	The Works of Edgar Allan Poe Volume 4 (of 5) o...	epubs/2150-0.txt	4
5	The Works of Edgar Allan Poe Volume 5 (of 5) o...	epubs/2151-0.txt	5

DOC.sample(10)

			para_str
book_id	chap_num	para_num
514	9	89	"Please forgive my rudeness, and come and danc...
	14	112	"The Rival Painters."
	23	42	"Bless me, what's all this?" cried the old lad...
3499	40	27	'Don't think of it, dear. Tell about the happy...
2788	29	22	“Now, Daisy!” called the high priest of Kitty ...
2786	10	61	“Who is it?” asked Jill, languidly, opening he...
2726	8	1	“I think I could, and I won't charge any inter...
2804	23	82	“Dear me, how you two have grown! You big thin...
2786	20	3	“Can't; we are off to Pebbly Beach the second ...
514	16	1	In the cold gray dawn the sisters lit their la...

Tokenize and Annotate

We use NLTK this time. Note that this process takes some time, mainly because the NLTK functions are not optimized for dataframes.

Note that we can choose between tokenizers. NLTK offers a variety of them. Here is a list.

def tokenize(doc_df, OHCO=OHCO, remove_pos_tuple=False, ws=False):
    
    # Paragraphs to Sentences
    df = doc_df.para_str\
        .apply(lambda x: pd.Series(nltk.sent_tokenize(x)))\
        .stack()\
        .to_frame()\
        .rename(columns={0:'sent_str'})
    
    # Sentences to Tokens
    # Local function to pick tokenizer
    def word_tokenize(x):
        if ws:
            s = pd.Series(nltk.pos_tag(nltk.WhitespaceTokenizer().tokenize(x)))
        else:
            s = pd.Series(nltk.pos_tag(nltk.word_tokenize(x))) # Discards stuff in between
        return s
            
    df = df.sent_str\
        .apply(word_tokenize)\
        .stack()\
        .to_frame()\
        .rename(columns={0:'pos_tuple'})
    
    # Grab info from tuple
    df['pos'] = df.pos_tuple.apply(lambda x: x[1])
    df['token_str'] = df.pos_tuple.apply(lambda x: x[0])
    if remove_pos_tuple:
        df = df.drop('pos_tuple', 1)
    
    # Add index
    df.index.names = OHCO
    
    return df

%%time
TOKEN = tokenize(DOC, ws=False)

Wall time: 1min 27s

TOKEN.head()

					pos_tuple	pos	token_str
book_id	chap_num	para_num	sent_num	token_num
2787	1	1	0	0	(“, IN)	IN	“
				1	(IT, NNP)	NNP	IT
				2	('S, POS)	POS	'S
				3	(time, NN)	NN	time
				4	(to, TO)	TO	to

TOKEN[TOKEN.pos.str.match('^NNP')]

					pos_tuple	pos	token_str
book_id	chap_num	para_num	sent_num	token_num
2787	1	1	0	1	(IT, NNP)	NNP	IT
				10	(Tom, NNP)	NNP	Tom
		2	0	1	(Come, NNP)	NNP	Come
		3	0	1	(Oh, NNP)	NNP	Oh
			1	23	(Polly, NNP)	NNP	Polly
...	...	...	...	...	...	...	...
5	10	107	0	9	(’, NNP)	NNP	’
			1	4	(A.M, NNP)	NNP	A.M
			7	10	(President, NNP)	NNP	President
			8	21	(Ponnonner, NNP)	NNP	Ponnonner
				22	(’, NNP)	NNP	’

54314 rows × 3 columns

Reduce

Extract a vocabulary from the TOKEN table

TOKEN['term_str'] = TOKEN['token_str'].str.lower().str.replace('[\W_]', '')

VOCAB = TOKEN.term_str.value_counts().to_frame()\
    .rename(columns={'index':'term_str', 'term_str':'n'})\
    .sort_index().reset_index().rename(columns={'index':'term_str'})
VOCAB.index.name = 'term_id'

VOCAB['num'] = VOCAB.term_str.str.match("\d+").astype('int')

VOCAB

	term_str	n	num
term_id
0		206017	0
1	0	1	1
2	00	1	1
3	00000258	1	1
4	0000157	1	1
...	...	...	...
29554	échapper	1	0
29555	élite	1	0
29556	émeutes	1	0
29557	été	1	0
29558	être	3	0

29559 rows × 3 columns

Annotate (VOCAB)

Add Stopwords

We use NLTK's built in stopword list for English. Note that we can add and subtract from this list, or just create our own list and keep it in our data model.

sw = pd.DataFrame(nltk.corpus.stopwords.words('english'), columns=['term_str'])
sw = sw.reset_index().set_index('term_str')
sw.columns = ['dummy']
sw.dummy = 1

sw.sample(10)

	dummy
term_str
between	1
didn't	1
about	1
few	1
them	1
during	1
you've	1
couldn't	1
who	1
needn't	1

VOCAB['stop'] = VOCAB.term_str.map(sw.dummy)
VOCAB['stop'] = VOCAB['stop'].fillna(0).astype('int')

VOCAB[VOCAB.stop == 1].sample(10)

	term_str	n	num	stop
term_id
13335	in	16973	0	1
17551	not	5774	0	1
18119	ours	41	0	1
12536	him	4593	0	1
1738	at	6738	0	1
29483	yourself	175	0	1
28976	whom	264	0	1
12198	having	618	0	1
26185	there	2611	0	1
11936	hadn	1	0	1

Add Stems

from nltk.stem.porter import PorterStemmer
stemmer1 = PorterStemmer()
VOCAB['stem_porter'] = VOCAB.term_str.apply(stemmer1.stem)

#from nltk.stem.snowball import SnowballStemmer
#stemmer2 = SnowballStemmer("english")
#VOCAB['stem_snowball'] = VOCAB.term_str.apply(stemmer2.stem)

#from nltk.stem.lancaster import LancasterStemmer
#stemmer3 = LancasterStemmer()
#VOCAB['stem_lancaster'] = VOCAB.term_str.apply(stemmer3.stem)

VOCAB.sample(10)
VOCAB.index.name = 'term_id'

#VOCAB[VOCAB.stem_porter != VOCAB.stem_lancaster]

#VOCAB['pos_max'] = TOKEN.pos.apply(lambda x: len(x.unique()))
#VOCAB['pos_max'] = TOKEN.pos.apply(lambda x: str.count())
#VOCAB['pos_max']  = TOKEN.pos.value_counts()
#VOCAB['pos_max']  = TOKEN.groupby('pos').size()
#VOCAB['pos_max']  = TOKEN.groupby('pos').mode()

MFCB = TOKEN.groupby(['term_str','pos']).pos.count().unstack().idxmax(1).to_frame()
VOCAB = pd.merge(VOCAB,MFCB, left_on='term_str', right_on = 'term_str')
VOCAB = VOCAB.rename(columns = {0:'pos_max'})

Save

VOCAB.sample(5)
VOCAB.index.name = 'term_id'

DOC.to_csv('DOC.csv')
LIB.to_csv('LIBRARY.csv')
VOCAB.to_csv('VOCAB.csv')
TOKEN.to_csv('TOKEN.csv')

TFIDF

count_method = 'n' # 'c' or 'n' # n = n tokens, c = distinct token (term) count
tf_method = 'sum' # sum, max, log, double_norm, raw, binary
tf_norm_k = .5 # only used for double_norm
idf_method = 'standard' # standard, max, smooth
gradient_cmap = 'YlGnBu' # YlGn, GnBu, YlGnBu; For tables; see https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html 

OHCO = ['book_id', 'chap_num', 'para_num', 'sent_num', 'token_num']
SENTS = OHCO[:4]
PARAS = OHCO[:3]
CHAPS = OHCO[:2]
BOOKS = OHCO[:1]

bag = CHAPS

pd.__version__

'1.0.0'

sns.set()
%matplotlib inline

Prepare the data

Import tables

Bring the the tables we created last time.

%%time
LIB = pd.read_csv("LIBRARY.csv").set_index(BOOKS)
TOKEN = pd.read_csv('TOKEN.csv').set_index(OHCO)
VOCAB = pd.read_csv('VOCAB.csv').set_index('term_id')
# DOC = pd.read_csv(data_dir + "DOC.csv").set_index(PARAS)

Wall time: 1.15 s

LIB = LIB[['book_title', 'book_file']]
LIB

	book_title	book_file
book_id
2787	An Old - fashioned Girl, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_AN_OLD-FASHIONED_GIRL...
2726	Eight Cousins, by Louisa M. Alcott	epubs_\ALCOTT_MAY_LOUISA_EIGHT_COUSINS-pg2726.txt
2786	Jack and Jill, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JACK_AND_JILL-pg2786.txt
3499	Jo's Boys, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JO’S_BOYS-pg3499.txt
2788	Little Men, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_MEN-pg2788.txt
514	Little Women, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_WOMEN-pg514.txt
2804	Rose in Bloom, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_ROSE_IN_BLOOM-pg2804.txt
3795	Under the Lilacs, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_UNDER_THE_LILACS-pg37...
1	The Works of Edgar Allan Poe Volume 1 (of 5) o...	epubs/2147-0.txt
2	The Works of Edgar Allan Poe Volume 2 (of 5) o...	epubs/2148-0.txt
3	The Works of Edgar Allan Poe Volume 3 (of 5) o...	epubs/2149-0.txt
4	The Works of Edgar Allan Poe Volume 4 (of 5) o...	epubs/2150-0.txt
5	The Works of Edgar Allan Poe Volume 5 (of 5) o...	epubs/2151-0.txt

VOCAB.head()

	term_str	n	num	stop	stem_porter	pos_max
term_id
0	NaN	206017	0	0	NaN	,
1	0	1	1	0	0	CD
2	00	1	1	0	00	CD
3	00000258	1	1	0	00000258	CD
4	0000157	1	1	0	0000157	CD

VOCAB = VOCAB[~VOCAB.term_str.isna()]

VOCAB.sample(5)

	term_str	n	num	stop	stem_porter	pos_max
term_id
18356	pail	28	0	0	pail	NN
523	adept	4	0	0	adept	NN
8081	dredging	1	0	0	dredg	VBG
25802	taking	239	0	0	take	VBG
26277	thought	1043	0	0	thought	VBD

TOKEN.head()

					pos_tuple	pos	token_str	term_str
book_id	chap_num	para_num	sent_num	token_num
2787	1	1	0	0	('“', 'IN')	IN	“	NaN
				1	('IT', 'NNP')	NNP	IT	it
				2	("'S", 'POS')	POS	'S	s
				3	('time', 'NN')	NN	time	time
				4	('to', 'TO')	TO	to	to

TOKEN = TOKEN[~TOKEN.term_str.isna()]

TOKEN.head()

					pos_tuple	pos	token_str	term_str
book_id	chap_num	para_num	sent_num	token_num
2787	1	1	0	1	('IT', 'NNP')	NNP	IT	it
				2	("'S", 'POS')	POS	'S	s
				3	('time', 'NN')	NN	time	time
				4	('to', 'TO')	TO	to	to
				5	('go', 'VB')	VB	go	go

# DOC.head()

Add term_id to TOKEN table

We need to do this to combine the VOCAB and TOKEN tables more efficiently. Note, we could have done this in the previous lab.

We use .map() because TOKEN and VOCAB do not share an index at this time.

VOCAB.reset_index().set_index('term_str').term_id term_str 0 1 1 2 10 3 100 4 1000 5 10000 6 1000000 7 10000000 8 10440 9 10800 10

TOKEN['term_id'] = TOKEN.term_str.map(VOCAB.reset_index().set_index('term_str').term_id)

TOKEN.head()

					pos_tuple	pos	token_str	term_str	term_id
book_id	chap_num	para_num	sent_num	token_num
2787	1	1	0	1	('IT', 'NNP')	NNP	IT	it	14145
				2	("'S", 'POS')	POS	'S	s	22463
				3	('time', 'NN')	NN	time	time	26439
				4	('to', 'TO')	TO	to	to	26510
				5	('go', 'VB')	VB	go	go	11429

Add Max POS to VOCAB

Just in case it's not there. It's easy now that we have a share feature -- term_id -- between VOCAB and TOKEN.

Regarding collisions when using .idxmax(), the documentation says "If multiple values equal the maximum, the first row label with that value is returned."

# Demo
# TOKEN.groupby(['term_id', 'pos']).pos.count()
# TOKEN.groupby(['term_id', 'pos']).pos.count().unstack()
# TOKEN.groupby(['term_id', 'pos']).pos.count().unstack().idxmax(1)

VOCAB['pos_max'] = TOKEN.groupby(['term_id', 'pos']).pos.count().unstack().idxmax(1)

VOCAB.sample(5)

	term_str	n	num	stop	stem_porter	pos_max
term_id
25412	sunbeams	2	0	0	sunbeam	JJ
13384	incitamentum	1	0	0	incitamentum	NN
20962	rakes	2	0	0	rake	NNS
7153	detect	14	0	0	detect	VB
10633	fountainhead	1	0	0	fountainhead	NN

Compare POS Stats in TOKEN and VOCAB

Pause and look at distribution of POS tags. The POS table could become part of your data model (analytical edition) if you were interested in studying POS tags.

POS = TOKEN.pos.value_counts().to_frame().rename(columns={'pos':'n'})
POS.index.name = 'pos_id'

POS.sort_values('n').plot.bar(y='n', figsize=(15,5), rot=45);

Zipf's Law

$f \propto \frac{1}{r} $

$k = fr$

Add Term Rank to VOCAB

if 'term_rank' not in VOCAB.columns:
    VOCAB = VOCAB.sort_values('n', ascending=False).reset_index()
    VOCAB.index.name = 'term_rank'
    VOCAB = VOCAB.reset_index()
    VOCAB = VOCAB.set_index('term_id')
    VOCAB['term_rank'] = VOCAB['term_rank'] + 1

VOCAB.head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max
term_id
26151	1	the	58042	0	1	the	DT
1110	2	and	41247	0	1	and	CC
17817	3	of	29442	0	1	of	IN
26510	4	to	28593	0	1	to	TO
219	5	a	26310	0	1	a	DT

Alternate Rank

The term_rank as defined above assigns different ranks to words with the same frequency, which occurs in the long tail, e.g. with words that appear once. This measure groups words by term count.

new_rank = VOCAB.n.value_counts()\
    .sort_index(ascending=False).reset_index().reset_index()\
    .rename(columns={'level_0':'term_rank2', 'index':'n', 'n':'nn'})\
    .set_index('n')

new_rank.head()

	term_rank2	nn
n
58042	0	1
41247	1	1
29442	2	1
28593	3	1
26310	4	1

VOCAB['term_rank2'] = VOCAB.n.map(new_rank.term_rank2) + 1

VOCAB.head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2
term_id
26151	1	the	58042	0	1	the	DT	1
1110	2	and	41247	0	1	and	CC	2
17817	3	of	29442	0	1	of	IN	3
26510	4	to	28593	0	1	to	TO	4
219	5	a	26310	0	1	a	DT	5

VOCAB['p'] = VOCAB.n / VOCAB.shape[0]

Compute Zipf's K

VOCAB['zipf_k'] = VOCAB.n * VOCAB.term_rank
VOCAB['zipf_k2'] = VOCAB.n * VOCAB.term_rank2
VOCAB['zipf_k3'] = VOCAB.p * VOCAB.term_rank2

VOCAB.describe().T

	count	mean	std	min	25%	50%	75%	max
term_rank	29556.0	14778.500000	8532.226614	1.000000	7389.750000	14778.500000	22167.250000	29556.000000
n	29556.0	37.567431	598.419437	1.000000	1.000000	3.000000	9.000000	58042.000000
num	29556.0	0.007376	0.085567	0.000000	0.000000	0.000000	0.000000	1.000000
stop	29556.0	0.005075	0.071060	0.000000	0.000000	0.000000	0.000000	1.000000
term_rank2	29556.0	589.034342	55.889659	1.000000	597.000000	603.000000	605.000000	605.000000
p	29556.0	0.001271	0.020247	0.000034	0.000034	0.000102	0.000305	1.963798
zipf_k	29556.0	48129.905535	26743.165360	19026.000000	26414.750000	38806.500000	64512.000000	160560.000000
zipf_k2	29556.0	7685.649411	18765.528221	605.000000	605.000000	1809.000000	5373.000000	160560.000000
zipf_k3	29556.0	0.260037	0.634914	0.020470	0.020470	0.061206	0.181790	5.432400

Words with low k

VOCAB[VOCAB.zipf_k <= VOCAB.zipf_k.quantile(.1)].sort_values('zipf_k3', ascending=True).head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	zipf_k2	zipf_k3
term_id
26076	19026	terminate	1	0	0	termin	VB	605	0.000034	19026	605	0.02047
1426	20991	archangel	1	0	0	archangel	NNP	605	0.000034	20991	605	0.02047
28447	20992	wadding	1	0	0	wad	NN	605	0.000034	20992	605	0.02047
1428	20993	archbishop	1	0	0	archbishop	NN	605	0.000034	20993	605	0.02047
1443	20994	architects	1	0	0	architect	NNS	605	0.000034	20994	605	0.02047

Words with high k

VOCAB[VOCAB.zipf_k >= VOCAB.zipf_k.quantile(.9)].sort_values('zipf_k3', ascending=False).head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	zipf_k2	zipf_k3
term_id
12226	16	he	10035	0	1	he	PRP	16	0.339525	160560	160560	5.432400
16095	39	me	4107	0	1	me	PRP	39	0.138957	160173	160173	5.419306
3719	35	by	4566	0	1	by	IN	35	0.154486	159810	159810	5.407024
17995	48	or	3322	0	1	or	CC	48	0.112397	159456	159456	5.395047
28820	47	were	3383	0	1	were	VBD	47	0.114461	159001	159001	5.379652

Visualize

Histogram of Zipf K

#px.histogram(VOCAB, 'zipf_k', marginal='box')

# px.histogram(VOCAB, 'zipf_k2', marginal='box')

# px.histogram(VOCAB, 'zipf_k3', marginal='box')

Rank and N

VSAMP1 = VOCAB[['n','term_rank','zipf_k','term_str','pos_max']]
# VSAMP2 = VOCAB[['n','term_rank2','zipf_k3']].drop_duplicates()

#px.scatter(VSAMP1, x='term_rank', y='n', log_y=False, log_x=False, hover_name='term_str', color='pos_max')

# px.scatter(VSAMP2, x='term_rank2', y='n', log_y=False, log_x=False)

#px.scatter(VSAMP1, x='term_rank', y='n', log_y=True, log_x=True, hover_name='term_str', color='pos_max')

# px.scatter(VSAMP2, x='term_rank2', y='n', log_y=True, log_x=True)

Demo Rank Index

rank_index = [1, 2, 3, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000]

demo = VOCAB.loc[VOCAB.term_rank.isin(rank_index), ['term_str', 'term_rank', 'n', 'zipf_k', 'pos_max']]

demo.style.background_gradient(cmap=gradient_cmap, high=.5)

	term_str	term_rank	n	zipf_k	pos_max
term_id
26151	the	1	58042	58042	DT
1110	and	2	41247	82494	CC
17817	of	3	29442	88326	IN
26145	that	10	11925	119250	IN
11934	had	20	6748	134960	VBD
7807	do	30	5242	157260	VBP
22518	said	40	3858	154320	VBD
1075	an	50	3066	153300	DT
5899	could	60	2611	156660	MD
16799	more	70	2134	149380	RBR
18189	over	80	1743	139440	IN
17340	never	90	1561	140490	RB
22721	say	100	1366	136600	VB
15478	love	200	722	144400	NN
27113	turned	300	413	123900	VBD
16110	means	400	296	118400	NNS
2674	betty	500	223	111500	NNP
15394	longer	600	182	109200	RBR
9246	everyone	700	153	107100	NN
24275	somewhat	800	133	106400	RB
23785	sisters	900	118	106200	NNS
14389	joy	1000	107	107000	NN
22113	rising	2000	48	96000	VBG
2293	bay	3000	30	90000	NN
13987	introduction	4000	21	84000	NN
26264	thorn	5000	15	75000	NN
23013	secondly	6000	12	72000	RB
17178	national	7000	9	63000	JJ
26766	transaction	8000	8	64000	NN

# rank_index = [1, 2, 3, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800]
# demo = VOCAB.loc[VOCAB.term_rank2.isin(rank_index), ['term_str', 'term_rank2', 'n', 'zipf_k2', 'pos_max']]
# demo.style.background_gradient(cmap=gradient_cmap, high=.5)

VOCAB Entropy

Compute P of VOCAB

This is the prior, or marginal, probability of a term.

%%time
VOCAB['p2'] = VOCAB.n / VOCAB.n.sum()

Wall time: 1.99 ms

Compute Entropy of VOCAB

VOCAB['h'] = VOCAB.p2 * np.log2(1/VOCAB.p2) # Self entropy of each word 
H = VOCAB.h.sum()
N_v = VOCAB.shape[0]
H_max = np.log2(N_v)
R = round(1 - (H/H_max), 2) * 100

print("H \t= {}\nH_max \t= {}\nR \t= {}%".format(H, H_max, int(R)))

H 	= 9.829010452294979
H_max 	= 14.851163413220117
R 	= 34%

BOW

BOW = TOKEN.groupby(bag+['term_id']).term_id.count()\
    .to_frame().rename(columns={'term_id':'n'})

BOW['c'] = BOW.n.astype('bool').astype('int')

BOW.head(10)
BOW.to_csv('BOW.csv')

Document-Term Matrix

We create a document-term count matrix. Note that we can create a matrix for any of the features in BOW. Also, see how the OHCO helps us distinguish between features and observation identity.

Note, these operations are slower than using groupby().

Create Count Matrix

%%time
DTCM = BOW[count_method].unstack().fillna(0).astype('int')

Wall time: 155 ms

DTCM.head()

	term_id	1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
book_id	chap_num
1	1	0	0	0	0	1	0	0	0	0	3	...	0	0	0	0	0	0	0	0	0	0
	2	1	0	0	0	0	0	0	0	0	4	...	0	0	0	0	0	0	0	0	0	0
	3	0	0	0	0	0	0	0	0	0	2	...	0	0	0	0	0	0	0	0	0	0
	4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
	5	0	0	0	0	0	0	0	0	0	2	...	0	0	0	0	0	0	0	1	0	0

5 rows × 29556 columns

Compute TF

We could also compute that using BOW.groupby().

%%time
print('TF method:', tf_method)

if tf_method == 'sum':
    TF = DTCM.T / DTCM.T.sum()

elif tf_method == 'max':
    TF = DTCM.T / DTCM.T.max()

elif tf_method == 'log':
    TF = np.log10(1 + DTCM.T)
    
elif tf_method == 'raw':
    TF = DTCM.T

elif tf_method == 'double_norm':
    TF = DTCM.T / DTCM.T.max()
    TF = tf_norm_k + (1 - tf_norm_k) * TF[TF > 0] # EXPLAIN; may defeat purpose of norming

elif tf_method == 'binary':
    TF = DTCM.T.astype('bool').astype('int')
    
TF = TF.T

TF method: sum
Wall time: 162 ms

TF.head()

	term_id	1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
book_id	chap_num
1	1	0.000000	0.0	0.0	0.0	0.000047	0.0	0.0	0.0	0.0	0.000140	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	2	0.000073	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000291	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	3	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000713	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	4	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	5	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000099	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000049	0.0	0.0

5 rows × 29556 columns

Compute DF

%%time
DF = DTCM[DTCM > 0].count()

Wall time: 283 ms

DF.head()

term_id
1    1
2    1
3    1
4    1
5    1
dtype: int64

Compute IDF

N = DTCM.shape[0]

print('IDF method:', idf_method)

if idf_method == 'standard':
    IDF = np.log10(N / DF)

elif idf_method == 'max':
    IDF = np.log10(DF.max() / DF) 

elif idf_method == 'smooth':
    IDF = np.log10((1 + N) / (1 + DF)) + 1 # Correct?

IDF method: standard

Compute TFIDF

TFIDF = TF * IDF

TFIDF.head()

	term_id	1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
book_id	chap_num
1	1	0.000000	0.0	0.0	0.0	0.000112	0.0	0.0	0.0	0.0	0.000126	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	2	0.000174	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000263	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	3	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000644	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	4	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	5	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000089	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000118	0.0	0.0

5 rows × 29556 columns

Move things to their places

VOCAB['df'] = DF
VOCAB['idf'] = IDF

VOCAB.head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	zipf_k2	zipf_k3	p2	h	df	idf
term_id
26151	1	the	58042	0	1	the	DT	1	1.963798	58042	58042	1.963798	0.052274	0.222570	247	0.001755
1110	2	and	41247	0	1	and	CC	2	1.395554	82494	82494	2.791108	0.037148	0.176474	247	0.001755
17817	3	of	29442	0	1	of	IN	3	0.996143	88326	88326	2.988429	0.026516	0.138865	247	0.001755
26510	4	to	28593	0	1	to	TO	4	0.967418	114372	114372	3.869671	0.025752	0.135947	247	0.001755
219	5	a	26310	0	1	a	DT	5	0.890175	131550	131550	4.450873	0.023695	0.127937	247	0.001755

%%time
BOW['tf'] = TF.stack()
BOW['tfidf'] = TFIDF.stack()

Wall time: 3.08 s

BOW.head()

			n	c	tf	tfidf
book_id	chap_num	term_id
1	1	5	1	1	0.000047	0.000112
		10	3	1	0.000140	0.000126
		13	1	1	0.000047	0.000098
		16	1	1	0.000047	0.000112
		17	1	1	0.000047	0.000112

Apply TFIDF sum to VOCAB

VOCAB['tfidf_sum'] = TFIDF.sum()

Observe results

VOCAB.sort_values('tfidf_sum', ascending=False).head(20).style.background_gradient(cmap=gradient_cmap, high=1)

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	zipf_k2	zipf_k3	p2	h	df	idf	tfidf_sum
term_id
19680	105	polly	1325	0	0	polli	NNP	104	0.044830	139125	137800	4.662336	0.001193	0.011588	26	0.979478	0.246200
14306	93	jo	1421	0	0	jo	NNP	92	0.048078	132153	130732	4.423197	0.001280	0.012299	71	0.543193	0.235562
17608	23	nt	6003	0	0	nt	RB	23	0.203106	138069	138069	4.671437	0.005406	0.040716	183	0.132001	0.182482
2541	193	ben	741	0	0	ben	NNP	185	0.025071	143013	137085	4.638145	0.000667	0.007040	31	0.903090	0.181365
22269	87	rose	1573	0	0	rose	NNP	86	0.053221	136851	135278	4.577006	0.001417	0.013406	118	0.322570	0.132242
6492	209	dan	675	0	0	dan	NNP	199	0.022838	141075	134325	4.544762	0.000608	0.006495	29	0.932054	0.120144
14290	276	jill	454	0	0	jill	NNP	256	0.015361	125304	116224	3.932332	0.000409	0.004602	23	1.032724	0.116546
26554	170	tom	819	0	0	tom	NNP	165	0.027710	139230	135135	4.572168	0.000738	0.007675	44	0.750999	0.114378
16174	215	meg	652	0	0	meg	NNP	204	0.022060	140180	133008	4.500203	0.000587	0.006303	46	0.731694	0.114222
19570	1287	plumfield	81	0	0	plumfield	NNP	525	0.002741	104247	42525	1.438794	0.000073	0.001003	34	0.862973	0.111802
15644	254	mac	525	0	0	mac	NNP	236	0.017763	133350	123900	4.192042	0.000473	0.005223	35	0.850384	0.105848
1995	355	bab	336	0	0	bab	NNP	309	0.011368	119280	103824	3.512789	0.000303	0.003538	24	1.014240	0.100857
19156	302	phebe	400	0	0	phebe	NNP	277	0.013534	120800	110800	3.748816	0.000360	0.004121	35	0.850384	0.087911
23386	18	she	7972	0	1	she	PRP	18	0.269725	143496	143496	4.855055	0.007180	0.051133	223	0.046147	0.085959
14178	233	jack	600	0	0	jack	NNP	218	0.020300	139800	130800	4.425497	0.000540	0.005865	65	0.581538	0.085879
14888	264	laurie	487	0	0	lauri	NNP	245	0.016477	128568	119315	4.036913	0.000439	0.004893	47	0.722354	0.085124
2651	375	beth	319	0	0	beth	NNP	320	0.010793	119625	102080	3.453783	0.000287	0.003380	29	0.932054	0.083636
1073	313	amy	378	0	0	ami	NNP	286	0.012789	118314	108108	3.657734	0.000340	0.003922	43	0.760983	0.078558
17172	270	nat	469	0	0	nat	NNP	250	0.015868	126630	117250	3.967046	0.000422	0.004735	34	0.862973	0.078477
12419	13	her	10838	0	1	her	PRP$	13	0.366694	140894	140894	4.767019	0.009761	0.065191	231	0.030840	0.078328

VOCAB[['term_rank','term_str','pos_max','tfidf_sum']]\
    .sort_values('tfidf_sum', ascending=False).head(50)\
    .style.background_gradient(cmap=gradient_cmap, high=1)

	term_rank	term_str	pos_max	tfidf_sum
term_id
19680	105	polly	NNP	0.246200
14306	93	jo	NNP	0.235562
17608	23	nt	RB	0.182482
2541	193	ben	NNP	0.181365
22269	87	rose	NNP	0.132242
6492	209	dan	NNP	0.120144
14290	276	jill	NNP	0.116546
26554	170	tom	NNP	0.114378
16174	215	meg	NNP	0.114222
19570	1287	plumfield	NNP	0.111802
15644	254	mac	NNP	0.105848
1995	355	bab	NNP	0.100857
19156	302	phebe	NNP	0.087911
23386	18	she	PRP	0.085959
14178	233	jack	NNP	0.085879
14888	264	laurie	NNP	0.085124
2651	375	beth	NNP	0.083636
1073	313	amy	NNP	0.078558
17172	270	nat	NNP	0.078477
12419	13	her	PRP$	0.078328
4183	453	celia	NNP	0.076972
11887	348	gutenberg	NNP	0.076073
4341	327	charlie	NNP	0.074761
853	352	alec	NNP	0.074319
29473	14	you	PRP	0.073084
27349	238	uncle	NNP	0.072490
2697	278	bhaer	NNP	0.071502
9698	345	fanny	NNP	0.071402
6910	301	demi	NNP	0.069364
3233	138	boys	NNS	0.068645
2674	500	betty	NNP	0.065724
26509	492	tm	NN	0.065367
26267	452	thorny	NNP	0.063242
1853	239	aunt	NNP	0.062122
20302	326	project	NNP	0.060939
10681	324	frank	NNP	0.059915
16940	98	mrs	NNP	0.058152
16939	109	mr	NNP	0.054054
15907	404	march	NNP	0.051623
16228	319	men	NNS	0.051253
16557	211	miss	NNP	0.051239
6459	376	daisy	NNP	0.050756
1436	562	archie	NNP	0.050355
16065	484	maud	NNP	0.049416
9683	392	fan	NNP	0.048854
16674	580	molly	NNP	0.046896
15306	85	ll	MD	0.043059
8005	414	dr	NNP	0.042937
11299	158	girls	NNS	0.042432
26568	464	tommy	NNP	0.042167

VOCAB.loc[VOCAB.pos_max != 'NNP', ['term_rank','term_str','pos_max','tfidf_sum']]\
    .sort_values('tfidf_sum', ascending=False)\
    .head(50).style.background_gradient(cmap=gradient_cmap, high=1)

	term_rank	term_str	pos_max	tfidf_sum
term_id
17608	23	nt	RB	0.182482
23386	18	she	PRP	0.085959
12419	13	her	PRP$	0.078328
29473	14	you	PRP	0.073084
3233	138	boys	NNS	0.068645
26509	492	tm	NN	0.065367
16228	319	men	NNS	0.051253
15306	85	ll	MD	0.043059
11299	158	girls	NNS	0.042432
26378	293	thus	RB	0.039245
25754	552	t	NN	0.038247
15637	92	m	VBP	0.037983
9764	249	father	NN	0.036329
3230	167	boy	NN	0.035364
15029	382	length	NN	0.034837
16868	144	mother	NN	0.031910
15127	214	life	NN	0.031462
8582	989	electronic	JJ	0.030963
29158	349	within	IN	0.030297
7838	524	dog	NN	0.030160
1614	128	asked	VBD	0.029380
4499	296	children	NNS	0.029338
28066	207	ve	VBP	0.029091
11296	210	girl	NN	0.028885
984	394	although	IN	0.028362
14574	737	king	NN	0.028316
7815	565	doctor	NN	0.027413
23774	409	sir	NN	0.027299
3737	230	ca	MD	0.026520
22813	401	school	NN	0.026443
3088	362	book	NN	0.026257
29175	204	wo	MD	0.026218
1208	169	answered	VBD	0.026033
16696	360	money	NN	0.025388
8653	541	em	PRP	0.025359
26426	147	till	NN	0.025151
17032	469	music	NN	0.024934
6111	206	cried	VBD	0.024694
15061	516	letter	NN	0.024538
28547	189	want	VBP	0.024504
26284	727	thousand	CD	0.024266
3002	785	boat	NN	0.024150
29262	566	works	NNS	0.024082
11429	91	go	VB	0.024064
6428	143	d	MD	0.023987
21106	273	read	VB	0.023937
11529	148	got	VBD	0.023506
15478	200	love	NN	0.023434
29249	162	work	NN	0.023309
13451	331	indeed	RB	0.023147

BOW = BOW.join(VOCAB[['term_str','pos_max']], on='term_id')

BOW.sort_values('tfidf', ascending=False).head(20)\
    .style.background_gradient(cmap=gradient_cmap, high=1)

			n	c	tf	tfidf	term_str	pos_max
book_id	chap_num	term_id
2788	21	19570	1	1	0.111111	0.095886	plumfield	NNP
		14306	1	1	0.111111	0.060355	jo	NNP
		16228	1	1	0.111111	0.029705	men	NNS
3	2	16802	23	1	0.010778	0.025807	morella	NNP
2804	44	11887	84	1	0.016932	0.022910	gutenberg	NNP
2788	21	3233	1	1	0.111111	0.022061	boys	NNS
2804	44	26509	57	1	0.011490	0.020594	tm	NN
5	9	3058	48	1	0.008158	0.019533	bonbon	NNP
4	10	7279	32	1	0.007891	0.018896	diddler	NN
2	6	27979	28	1	0.007867	0.018838	valdemar	NNP
2726	24	11887	84	1	0.013503	0.018270	gutenberg	NNP
5	4	12717	27	1	0.007452	0.017844	hopfrog	NNP
	8	18669	19	1	0.007422	0.017771	pathrick	NNP
2787	12	19680	100	1	0.018119	0.017747	polly	NNP
2804	44	20302	84	1	0.016932	0.017173	project	NNP
3795	24	11887	84	1	0.012162	0.016455	gutenberg	NNP
2726	24	26509	57	1	0.009163	0.016423	tm	NN
2	5	27958	34	1	0.009081	0.016277	v	NN
2787	7	19680	85	1	0.016393	0.016057	polly	NNP
	5	19680	83	1	0.016271	0.015937	polly	NNP

Visualize

Rank and TFIDF Sum

#px.scatter(VOCAB, x='term_rank', y='tfidf_sum', hover_name='term_str', hover_data=['n'], color='pos_max')

# px.scatter(VOCAB, x='term_rank2', y='tfidf_sum', hover_name='term_str', hover_data=['n'], color='pos_max')

Log Rank and Log TFIDF Sum

#px.scatter(VOCAB, x='term_rank', y='tfidf_sum', hover_name='term_str', hover_data=['n'], color='pos_max', log_x=True, log_y=True)

# px.scatter(VOCAB, x='term_rank2', y='tfidf_sum', hover_name='term_str', hover_data=['n'], color='pos_max', 
#            log_x=True, log_y=True)

Show Demo Table with TFIDF

demo2 = VOCAB.loc[VOCAB.term_rank.isin(rank_index), ['term_str', 'pos_max', 'term_rank', 'n', 'zipf_k', 'tfidf_sum']]

demo2.style.background_gradient(cmap=gradient_cmap, high=1)

	term_str	pos_max	term_rank	n	zipf_k	tfidf_sum
term_id
26151	the	DT	1	58042	58042	0.022318
1110	and	CC	2	41247	82494	0.016572
17817	of	IN	3	29442	88326	0.011051
26145	that	IN	10	11925	119250	0.004624
11934	had	VBD	20	6748	134960	0.005251
7807	do	VBP	30	5242	157260	0.014852
22518	said	VBD	40	3858	154320	0.010826
1075	an	DT	50	3066	153300	0.002332
5899	could	MD	60	2611	156660	0.003041
16799	more	RBR	70	2134	149380	0.002462
18189	over	IN	80	1743	139440	0.005032
17340	never	RB	90	1561	140490	0.007722
22721	say	VB	100	1366	136600	0.008108
15478	love	NN	200	722	144400	0.023434
27113	turned	VBD	300	413	123900	0.011823
16110	means	NNS	400	296	118400	0.019283
2674	betty	NNP	500	223	111500	0.065724
15394	longer	RBR	600	182	109200	0.013684
9246	everyone	NN	700	153	107100	0.020609
24275	somewhat	RB	800	133	106400	0.013636
23785	sisters	NNS	900	118	106200	0.017404
14389	joy	NN	1000	107	107000	0.013036
22113	rising	VBG	2000	48	96000	0.009151
2293	bay	NN	3000	30	90000	0.007658
13987	introduction	NN	4000	21	84000	0.004541
26264	thorn	NN	5000	15	75000	0.003985
23013	secondly	RB	6000	12	72000	0.003485
17178	national	JJ	7000	9	63000	0.003174
26766	transaction	NN	8000	8	64000	0.003490

#px.scatter(demo2, x='term_rank', y='tfidf_sum', log_x=True, log_y=True, text='term_str', color='pos_max', size='n')

Word-Context Matrix Entropy

WCM = DTCM / DTCM.sum()

WCM.sum().head()

term_id
1    1.0
2    1.0
3    1.0
4    1.0
5    1.0
dtype: float64

WCMh = WCM * np.log2(1/WCM)

VOCAB['h2'] = WCMh.sum()

VOCAB['h2'].hist();

X Factor

# VOCAB['x_factor'] = np.log(VOCAB.term_rank) * VOCAB.h2

# px.scatter(VOCAB, x='term_rank', y='x_factor', hover_name='term_str', color='pos_max', hover_data=['n'])

# VOCAB['x_factor2'] = VOCAB.term_rank2 * VOCAB.h2

VOCAB['x_factor2'] = np.log(VOCAB.term_rank2) * VOCAB.h2

px.scatter(VOCAB, x='term_rank2', y='x_factor2', hover_name='term_str', color='pos_max', hover_data=['n'])

# px.scatter(VOCAB, x='term_rank', y='x_factor', log_x=True, log_y=True, hover_name='term_str', color='pos_max', hover_data=['n'])

# px.scatter(VOCAB, x='term_rank2', y='x_factor2', log_x=True, log_y=True, hover_name='term_str', color='pos_max', hover_data=['n'])

Demo Table

# demo3 = VOCAB.loc[VOCAB.term_rank.isin(rank_index), ['term_str', 'pos_max', 'n', 'term_rank', 'zipf_k', 'tfidf_sum', 'h2', 'x_factor', 'term_rank2', 'x_factor2']]

# demo3.style.background_gradient(cmap=gradient_cmap)

# px.scatter(demo3, x='term_rank', y='x_factor', log_x=True, log_y=True, text='term_str', color='pos_max', size='n')

# px.scatter(demo3, x='term_rank2', y='x_factor2', log_x=False, log_y=False, text='term_str', color='pos_max', size='n')

Reduce VOCAB

Select Significant Terms based on X Factor

We want to take the upper and middle segment of our graph.

# key_col = 'tfidf_sum'
key_col = 'x_factor2'
key_min = VOCAB[key_col].quantile(.9)
rank_min = 200

SIGS = VOCAB.loc[(VOCAB[key_col] >= key_min) & (VOCAB.term_rank >= rank_min)].sort_values(key_col, ascending=False)

SIGS.shape[0]

2805

SIGS[['pos_max', 'term_str', 'n', 'term_rank', 'zipf_k', 'df', 'idf', 'tfidf_sum','x_factor2']].head(100).style.background_gradient(cmap=gradient_cmap, high=1)

	pos_max	term_str	n	term_rank	zipf_k	df	idf	tfidf_sum	x_factor2
term_id
14967	VB	leave	253	457	115621	147	0.227134	0.013890	41.379320
12364	VBD	held	265	436	115540	150	0.218360	0.013137	41.372233
4128	VBD	caught	197	569	112093	129	0.283862	0.013035	41.343166
8641	RB	else	238	479	114002	140	0.248324	0.013819	41.252227
27111	NN	turn	278	419	116482	153	0.209760	0.012972	41.163218
25802	VBG	taking	239	477	114003	144	0.236089	0.013082	41.110264
5100	VBG	coming	309	384	118656	158	0.195795	0.014899	41.076993
29199	VB	wonder	204	555	113220	131	0.277180	0.013006	41.061266
3458	VBD	brought	332	358	118856	168	0.169142	0.012631	41.058465
8377	JJ	easy	198	567	112266	126	0.294081	0.013113	41.043533
24024	NN	smile	245	468	114660	140	0.248324	0.014923	41.030358
26232	VBG	thinking	224	499	111776	139	0.251437	0.012957	41.026709
27113	VBD	turned	413	300	123900	188	0.120294	0.011823	40.987424
1510	NNS	arms	256	451	115456	142	0.242163	0.014446	40.976655
21812	NN	rest	336	356	119616	166	0.174344	0.013432	40.903754
24569	VBD	spoke	338	351	118638	169	0.166565	0.012842	40.835396
23786	VB	sit	180	605	108900	119	0.318905	0.013318	40.824385
15766	VBG	making	304	391	118864	159	0.193055	0.013106	40.783993
23791	VBG	sitting	202	557	112514	124	0.301030	0.014745	40.778128
26534	RB	together	321	370	118770	160	0.190332	0.013484	40.768746
12510	JJ	high	246	465	114390	141	0.245233	0.013880	40.763012
9133	RB	especially	213	522	111186	130	0.280508	0.013277	40.761148
22669	NN	satisfaction	168	641	107688	114	0.337547	0.012692	40.760650
23501	JJ	short	233	485	113005	137	0.257731	0.013594	40.758889
3374	JJ	bright	228	494	112632	132	0.273878	0.014627	40.721521
9313	IN	except	177	616	109032	119	0.318905	0.012641	40.720122
25317	RB	suddenly	287	407	116809	150	0.218360	0.014280	40.713335
17258	VBP	need	319	377	120263	159	0.193055	0.014783	40.712745
13885	NN	interest	282	415	117030	145	0.233084	0.015143	40.672142
6381	VBD	cut	218	511	111398	130	0.280508	0.012954	40.660302
20990	VBD	ran	254	455	115570	142	0.242163	0.014389	40.654382
17846	RB	often	356	330	117480	168	0.169142	0.013646	40.645111
7855	VBG	doing	240	475	114000	135	0.264118	0.014233	40.630317
23648	NN	sight	267	431	115077	148	0.224190	0.013172	40.626689
11478	VBN	gone	335	357	119595	159	0.193055	0.015586	40.614491
17369	JJ	next	387	309	119583	172	0.158923	0.014021	40.603242
1256	JJ	anxious	232	486	112752	132	0.273878	0.015090	40.577229
14840	JJ	late	238	478	113764	135	0.264118	0.014172	40.564354
22490	JJ	sad	146	730	106580	109	0.357025	0.012841	40.549052
24983	VBD	stood	476	267	127092	197	0.099985	0.011433	40.546933
10410	VBD	followed	224	498	111552	132	0.273878	0.014177	40.544219
16333	VBD	met	164	653	107092	110	0.353059	0.013160	40.535228
17296	DT	neither	167	643	107381	113	0.341373	0.013819	40.529700
11320	VBG	giving	164	656	107584	112	0.345234	0.012053	40.521688
26169	PRP	themselves	259	445	115255	147	0.227134	0.012990	40.521413
24536	NN	spite	245	467	114415	137	0.257731	0.014241	40.514991
3639	NN	burst	132	808	106656	104	0.377418	0.012156	40.513939
14870	VBD	laughed	351	340	119340	162	0.184937	0.014950	40.499623
2468	IN	behind	354	336	118944	166	0.174344	0.014123	40.491627
11227	VBG	getting	297	399	118503	150	0.218360	0.014225	40.489958
26973	NN	trouble	260	443	115180	140	0.248324	0.014623	40.486869
25141	JJ	strong	269	428	115132	147	0.227134	0.013764	40.483346
16491	NN	minute	395	305	120475	178	0.144032	0.012957	40.466904
14976	VBD	led	186	592	110112	121	0.311666	0.012819	40.432386
22672	JJ	satisfied	167	645	107715	114	0.337547	0.013063	40.421110
8914	RB	enough	433	290	125570	176	0.148939	0.014656	40.375696
6862	NN	delight	166	648	107568	111	0.349129	0.014193	40.359998
21120	JJ	real	230	490	112700	133	0.270600	0.013957	40.352634
23164	VBD	sent	235	483	113505	129	0.283862	0.015635	40.344609
24025	VBD	smiled	146	732	106872	106	0.369146	0.012797	40.342451
10536	VB	forget	231	488	112728	131	0.277180	0.014667	40.334386
23054	VBG	seeing	178	609	108402	114	0.337547	0.013560	40.331735
3393	VB	bring	249	462	115038	138	0.254573	0.014214	40.330908
8340	JJ	early	187	589	110143	117	0.326266	0.013827	40.326784
26446	NNS	times	301	395	118895	154	0.206931	0.013623	40.320339
19521	JJ	pleased	220	507	111540	125	0.297542	0.014840	40.312398
947	RB	almost	202	558	112716	124	0.301030	0.013199	40.306497
12264	VB	hear	355	332	117860	157	0.198552	0.016450	40.300014
14525	VBD	kept	389	308	119812	165	0.176968	0.015884	40.290441
23534	VB	show	351	341	119691	162	0.184937	0.014958	40.285910
23285	JJ	several	353	337	118961	162	0.184937	0.014210	40.272354
2499	VB	believe	317	379	120143	153	0.209760	0.014038	40.256824
24422	VB	speak	248	463	114824	134	0.267347	0.014622	40.243809
11314	VBN	given	270	427	115290	142	0.242163	0.014405	40.228508
9851	VB	feel	452	280	126560	187	0.122610	0.012990	40.227604
23974	RB	slowly	152	705	107160	106	0.369146	0.013042	40.225733
5815	NN	corner	195	572	111540	116	0.329994	0.014800	40.198055
18620	VBD	passed	205	550	112750	122	0.308092	0.014769	40.195698
22725	VBG	saying	418	297	124146	172	0.158923	0.015313	40.180512
19525	NN	pleasure	232	487	112984	125	0.297542	0.015821	40.166538
18374	NN	pair	186	593	110298	114	0.337547	0.014796	40.157696
18108	MD	ought	273	425	116025	134	0.267347	0.017093	40.151251
17499	NN	none	136	783	106488	101	0.390130	0.012633	40.145026
23729	IN	since	354	334	118236	160	0.190332	0.015106	40.130726
18390	JJ	pale	153	699	106947	105	0.373262	0.014274	40.127088
28573	JJ	warm	140	754	105560	102	0.385852	0.013208	40.111028
29128	VBP	wish	417	298	124266	167	0.171735	0.016179	40.076606
23644	NN	sigh	120	877	105240	95	0.416728	0.012154	40.072048
29266	NN	world	493	262	129166	194	0.106650	0.011882	40.056827
3671	JJ	busy	243	473	114939	128	0.287242	0.016474	40.052363
12095	RB	hardly	140	758	106120	102	0.385852	0.012202	40.052001
9865	VBD	fell	430	291	125130	178	0.144032	0.014612	40.048955
18101	NNS	others	323	368	118864	153	0.209760	0.014350	40.041039
21128	RB	really	389	307	119423	169	0.166565	0.014395	40.031381
25316	JJ	sudden	253	459	116127	133	0.270600	0.016176	40.025148
27425	VB	understand	208	537	111696	121	0.311666	0.014152	40.020386
10071	JJ	fine	385	310	119350	166	0.174344	0.015618	40.014417
23252	VBN	set	499	261	130239	191	0.113418	0.012745	40.001146
23998	JJ	small	444	286	126984	178	0.144032	0.014304	39.999989
1612	VB	ask	289	405	117045	139	0.251437	0.016435	39.998588

Save Work

VOCAB.to_csv('VOCAB2.csv')
TOKEN.to_csv('TOKEN2.csv')
BOW.to_csv('DOC2.csv')
DTCM.to_csv('DTCM.csv')
TFIDF.to_csv('TFIDF.csv')
#SIGS.to_csv('SIGS.csv')
#WCM.to_csv('WCM.csv')
# BOW.to_csv('BOW.csv')

Similarity and Distance Measures

Config

We define two OHCO lists, one to match the reduced TFIDF table we are importing, and the other to define the table after we compress this table to make clustering easier.

OHCO_src = ['book_id', 'chap_num']
OHCO = ['book_id']

Import

import pandas as pd
import numpy as np
import re 
from numpy.linalg import norm
from scipy.spatial.distance import pdist

import seaborn as sns
sns.set(style="ticks")

%matplotlib inline

Prepare the Data

Import tables

TFIDF = pd.read_csv('TFIDF.csv').set_index(OHCO_src)
VOCAB = pd.read_csv('VOCAB.csv').set_index('term_id')
LIB = pd.read_csv('LIBRARY.csv').set_index('book_id')

TFIDF.head()

		1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
book_id	chap_num
1	1	0.000000	0.0	0.0	0.0	0.000112	0.0	0.0	0.0	0.0	0.000126	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	2	0.000174	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000263	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	3	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000644	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	4	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0
	5	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.000089	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000118	0.0	0.0

5 rows × 29556 columns

Add Some Labels to the Library

LIB.loc[LIB.book_title.str.contains('Alcott'), 'author'] = 'Alcott'
LIB['author'] = LIB['author'].fillna('Poe')
LIB['title'] = LIB.book_title.str.split(', by').apply(lambda x: x[0])
LIB.columns = ['book_id','book_title','book_file','author','title']

LIB

	book_id	book_title	book_file	author	title
book_id
2787	An Old - fashioned Girl, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_AN_OLD-FASHIONED_GIRL...	2787	Alcott	An Old - fashioned Girl
2726	Eight Cousins, by Louisa M. Alcott	epubs_\ALCOTT_MAY_LOUISA_EIGHT_COUSINS-pg2726.txt	2726	Alcott	Eight Cousins
2786	Jack and Jill, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JACK_AND_JILL-pg2786.txt	2786	Alcott	Jack and Jill
3499	Jo's Boys, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JO’S_BOYS-pg3499.txt	3499	Alcott	Jo's Boys
2788	Little Men, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_MEN-pg2788.txt	2788	Alcott	Little Men
514	Little Women, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_WOMEN-pg514.txt	514	Alcott	Little Women
2804	Rose in Bloom, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_ROSE_IN_BLOOM-pg2804.txt	2804	Alcott	Rose in Bloom
3795	Under the Lilacs, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_UNDER_THE_LILACS-pg37...	3795	Alcott	Under the Lilacs
1	The Works of Edgar Allan Poe Volume 1 (of 5) o...	epubs/2147-0.txt	1	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) o...
2	The Works of Edgar Allan Poe Volume 2 (of 5) o...	epubs/2148-0.txt	2	Poe	The Works of Edgar Allan Poe Volume 2 (of 5) o...
3	The Works of Edgar Allan Poe Volume 3 (of 5) o...	epubs/2149-0.txt	3	Poe	The Works of Edgar Allan Poe Volume 3 (of 5) o...
4	The Works of Edgar Allan Poe Volume 4 (of 5) o...	epubs/2150-0.txt	4	Poe	The Works of Edgar Allan Poe Volume 4 (of 5) o...
5	The Works of Edgar Allan Poe Volume 5 (of 5) o...	epubs/2151-0.txt	5	Poe	The Works of Edgar Allan Poe Volume 5 (of 5) o...

Collapse TFIDF

We want to work with larger bags in this notebook, in order to better visualize our resulting clusters.

TFIDF = TFIDF.groupby(OHCO).mean()

TFIDF

	1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
book_id
1	0.000022	0.000000	0.000000	0.000000	0.000014	0.000000	0.000000	0.000000	0.000000	0.000184	...	0.00010	0.000057	0.000057	0.000000	0.000000	0.000000	0.000000	0.000015	0.000000	0.000000
2	0.000000	0.000000	0.000023	0.000023	0.000000	0.000000	0.000000	0.000000	0.000000	0.000097	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
3	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
4	0.000000	0.000034	0.000000	0.000000	0.000000	0.000034	0.000034	0.000000	0.000000	0.000157	...	0.00001	0.000000	0.000000	0.000077	0.000077	0.000012	0.000012	0.000000	0.000012	0.000041
5	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000097	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000054
514	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2726	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000013	0.000013	0.000012	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2786	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000011	0.000011	0.000019	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2787	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2788	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2804	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000018	0.000018	0.000017	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
3499	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
3795	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000061	...	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

13 rows × 29556 columns

Create a DOC table

We want to create a new table that maps the OHCO levels to a single doc_id. We do this so that when we create a table to store pairs of docs and their distances, we can use a single-valued ID for each docs.

This table will also be used to store cluster assignments.

All of this will become clearer below!

DOC = TFIDF.reset_index()[OHCO] # We create a table from the OHCO in our TFIDF table

DOC.head()

	book_id
0	1
1	2
2	3
3	4
4	5

DOC.index.name = 'doc_id' # We give the new index a name

DOC.head()

	book_id
doc_id
0	1
1	2
2	3
3	4
4	5

Add a meaningful Title to DOC index

DOC['title'] = DOC.book_id.map(LIB.author) \
    + '-' + DOC[OHCO].apply(lambda x: x.astype('str').str.cat(sep='-'), 1) \
    + ': '+ DOC.book_id.map(LIB.title) 

DOC

	book_id	title
doc_id
0	1	Poe-1: The Works of Edgar Allan Poe Volume 1 (...
1	2	Poe-2: The Works of Edgar Allan Poe Volume 2 (...
2	3	Poe-3: The Works of Edgar Allan Poe Volume 3 (...
3	4	Poe-4: The Works of Edgar Allan Poe Volume 4 (...
4	5	Poe-5: The Works of Edgar Allan Poe Volume 5 (...
5	514	Alcott-514: Little Women
6	2726	Alcott-2726: Eight Cousins
7	2786	Alcott-2786: Jack and Jill
8	2787	Alcott-2787: An Old - fashioned Girl
9	2788	Alcott-2788: Little Men
10	2804	Alcott-2804: Rose in Bloom
11	3499	Alcott-3499: Jo's Boys
12	3795	Alcott-3795: Under the Lilacs

Create Normalized Tables

L0 = TFIDF.astype('bool').astype('int')
L1 = TFIDF.apply(lambda x: x / x.sum(), 1)
L2 = TFIDF.apply(lambda x: x / norm(x), 1)

# ((L2.T)**2).sum()

Create Doc Pair Table

Create a table to store our results.

Note that pist() is a "distance matrix computation from a collection of raw observation vectors stored in a rectangular array".

PAIRS = pd.DataFrame(index=pd.MultiIndex.from_product([DOC.index.tolist(), DOC.index.tolist()])).reset_index()
PAIRS = PAIRS[PAIRS.level_0 < PAIRS.level_1].set_index(['level_0','level_1'])
PAIRS.index.names = ['doc_a', 'doc_b']

PAIRS.shape

(78, 0)

PAIRS.head()

doc_a	doc_b
0	1
	2
	3
	4
	5

Compute Distances

%time PAIRS['cityblock'] = pdist(TFIDF, 'cityblock')

Wall time: 6.98 ms

%time PAIRS['euclidean'] = pdist(TFIDF, 'euclidean')

Wall time: 7.98 ms

%time PAIRS['cosine'] = pdist(TFIDF, 'cosine')

Wall time: 7.98 ms

%time PAIRS['jaccard'] = pdist(L0, 'jaccard') # Fast, and similar to js

Wall time: 20.9 ms

%time PAIRS['dice'] = pdist(L0, 'dice')

Wall time: 4.99 ms

# %time PAIRS['js'] = pdist(L1, 'jensenshannon') # Turns out to be really slow                               

%time PAIRS['euclidean2'] = pdist(L2, 'euclidean') # Should be the same as cosine (colinear)

Wall time: 4.99 ms

%time PAIRS['js'] = pdist(TFIDF, 'jensenshannon')

Wall time: 28.9 ms

import scipy
scipy.__version__

'1.3.2'

PAIRS.head()

		cityblock	euclidean	cosine	jaccard	dice	euclidean2	js
doc_a	doc_b
0	1	0.529754	0.007972	0.599803	0.641180	0.471865	1.095265	0.582456
	2	0.611615	0.011209	0.757811	0.712373	0.553245	1.231106	0.646053
	3	0.582552	0.008748	0.676188	0.656640	0.488804	1.162917	0.610223
	4	0.645247	0.011271	0.782488	0.702160	0.541023	1.250990	0.651606
	5	0.576153	0.011543	0.919940	0.737200	0.583782	1.356422	0.695814

Compare Distributions

if PAIRS.shape[0] > 1000:
    SAMPLE = PAIRS.sample(1000)
else:
    SAMPLE = PAIRS

sns.pairplot(SAMPLE)

<seaborn.axisgrid.PairGrid at 0x2470c600fd0>

PAIRS.sort_values('cosine').head(20).style.background_gradient('YlGn', high=1)

		cityblock	euclidean	cosine	jaccard	dice	euclidean2	js
doc_a	doc_b
6	10	0.279733	0.004397	0.203373	0.571110	0.399688	0.637767	0.506003
9	11	0.320440	0.007974	0.408351	0.582797	0.411230	0.903716	0.527273
0	1	0.529754	0.007972	0.599803	0.641180	0.471865	1.095265	0.582456
5	11	0.325244	0.008646	0.604417	0.587026	0.415454	1.099470	0.538363
1	3	0.533722	0.007259	0.611246	0.632314	0.462324	1.105664	0.583025
5	9	0.309606	0.010875	0.665723	0.565483	0.394197	1.153883	0.539526
1	2	0.552469	0.009888	0.674817	0.705388	0.544864	1.161737	0.610005
0	3	0.582552	0.008748	0.676188	0.656640	0.488804	1.162917	0.610223
1	4	0.606536	0.010094	0.729115	0.707712	0.547643	1.207572	0.633531
2	3	0.614765	0.010604	0.749064	0.723946	0.567331	1.223980	0.641860
3	4	0.633443	0.010484	0.749497	0.704986	0.544384	1.224334	0.640562
6	11	0.342550	0.008149	0.754548	0.599891	0.428461	1.228452	0.561977
10	11	0.321504	0.008077	0.757095	0.587838	0.416268	1.230524	0.547727
0	2	0.611615	0.011209	0.757811	0.712373	0.553245	1.231106	0.646053
	4	0.645247	0.011271	0.782488	0.702160	0.541023	1.250990	0.651606
6	7	0.313452	0.009562	0.785452	0.548156	0.377559	1.253357	0.538337
7	11	0.341281	0.009285	0.798321	0.587712	0.416141	1.263583	0.555243
	10	0.324154	0.009572	0.799013	0.577875	0.406346	1.264131	0.553234
5	6	0.323584	0.010286	0.820897	0.593285	0.421752	1.281325	0.559031
2	4	0.659703	0.012709	0.824429	0.709710	0.550039	1.284079	0.670374

# def get_comps(doc_id):
#     idx = pd.IndexSlice
#     a = PAIRS.loc[idx[doc_id, :], :]
#     b = PAIRS.loc[idx[:, doc_id], :]
#     return pd.concat([a,b])

Create Clusters

Hiearchical

import scipy.cluster.hierarchy as sch
import matplotlib.pyplot as plt

def hca(sims, linkage_method='ward', color_thresh=.3, figsize=(10, 10)):
    tree = sch.linkage(sims, method=linkage_method)
    labels = DOC.title.values
    plt.figure()
    fig, axes = plt.subplots(figsize=figsize)
    dendrogram = sch.dendrogram(tree, 
                                labels=labels, 
                                orientation="left", 
                                count_sort=True,
                                distance_sort=True,
                                above_threshold_color='.75',
                                color_threshold=color_thresh
                               )
    plt.tick_params(axis='both', which='major', labelsize=14)

hca(PAIRS.cosine, color_thresh=1)

<Figure size 432x288 with 0 Axes>

hca(PAIRS.jaccard, color_thresh=.6)

<Figure size 432x288 with 0 Axes>

hca(PAIRS.euclidean, color_thresh=.3)

<Figure size 432x288 with 0 Axes>

hca(PAIRS.cityblock, color_thresh=8)

<Figure size 432x288 with 0 Axes>

hca(PAIRS.js, color_thresh=.6)

<Figure size 432x288 with 0 Axes>

K-Means

K-Means only uses Euclidean distance. Why?

• The K-Means procedure does not explicitly use pairwise distances between data points.
• Instead, it repeatedly assigns points to the closest centroid thereby using Euclidean distance from data points to a centroid.
• However, K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points.
• The term "centroid" is itself from Euclidean geometry. It is multivariate mean in Euclidean space. Euclidean space is about euclidean distances. Non-Euclidean distances will generally not span Euclidean space. That's why K-Means is for Euclidean distances only.

See the Cross Validated post on this.

from sklearn.cluster import KMeans

n_clusters = 4

DOC['y_raw'] = KMeans(n_clusters).fit_predict(TFIDF)
DOC['y_L0'] = KMeans(n_clusters).fit_predict(L0)
DOC['y_L1'] = KMeans(n_clusters).fit_predict(L1)
DOC['y_L2'] = KMeans(n_clusters).fit_predict(L2)

DOC.sort_values('y_raw').style.background_gradient(cmap='YlGn', high=1)

	book_id	title	y_raw	y_L0	y_L1	y_L2
doc_id
12	3795	Alcott-3795: Under the Lilacs	0	0	3	3
5	514	Alcott-514: Little Women	1	0	0	0
6	2726	Alcott-2726: Eight Cousins	1	0	0	3
7	2786	Alcott-2786: Jack and Jill	1	0	0	0
9	2788	Alcott-2788: Little Men	1	0	2	0
10	2804	Alcott-2804: Rose in Bloom	1	0	0	3
11	3499	Alcott-3499: Jo's Boys	1	0	0	0
8	2787	Alcott-2787: An Old - fashioned Girl	2	0	1	2
0	1	Poe-1: The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	3	1	0	1
1	2	Poe-2: The Works of Edgar Allan Poe Volume 2 (of 5) of the Raven Edition	3	2	0	1
2	3	Poe-3: The Works of Edgar Allan Poe Volume 3 (of 5) of the Raven Edition	3	2	0	1
3	4	Poe-4: The Works of Edgar Allan Poe Volume 4 (of 5) of the Raven Edition	3	3	0	1
4	5	Poe-5: The Works of Edgar Allan Poe Volume 5 (of 5) of the Raven Edition	3	2	0	1

Save Work

DOC.to_csv('DOC3.csv')
LIB.to_csv('LIB3.csv')
TFIDF.to_csv('TFIDF_book_final.csv')

Principal Component Analysis

OHCO = ['book_id', 'chap_num']

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from scipy.linalg import norm

import plotly_express as px
import seaborn as sns

sns.set(style='ticks')
%matplotlib inline

LIB = pd.read_csv('LIB3.csv').set_index('book_id')
VOCAB = pd.read_csv('VOCAB2.csv').set_index('term_id')
TFIDF = pd.read_csv('TFIDF.csv').set_index(OHCO)

TFIDF.shape

(248, 29556)

Normalize doc vector lengths

We use L2 normalization

# TFIDF = TFIDF.apply(lambda x: x / norm(x, 2), 1)

TFIDF = TFIDF.apply(lambda x: x / np.sqrt(np.square(x).sum()), 1)

Normalize term vector variance

We do not normalize variance, which we would normally do, such as with data containing divergent units of measure. \ This is because to do so would exaggerate the importance of rare words (see Ng, 2008: 6m40s — 8m00s).

Center the word vectors

Note that we are taking the column-wise means -- the means for the term vectors. \ We don't really need to do this. But it is typical for PCA. \ NOTE: Some argue that centering alters the cosine angles.

TFIDF = TFIDF - TFIDF.mean()

Compute Covariance Matrix

$n = |X| = |Y|$

$Cov(X,Y) = \dfrac{\sum_{i=1}^{n} (x_i - \mu_X) (y_i - \mu_Y)}{n - 1} = \dfrac{XY}{n-1}$

We could we use the built in Pandas method here, but compute it ourselves.

# COV = TFIDF.cov() # This also centers the vectors
COV = TFIDF.T.dot(TFIDF) / (TFIDF.shape[0] - 1)

COV.head()

	1	2	3	4	5	6	7	8	9	10	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
1	3.759325e-07	-5.998677e-09	-4.502953e-09	-4.502953e-09	-1.493126e-09	-5.998677e-09	-5.998677e-09	-4.176768e-09	-4.176768e-09	4.916907e-07	...	-7.653323e-09	-3.180492e-09	-3.180492e-09	-7.837017e-09	-7.837017e-09	-2.392873e-09	-2.392873e-09	-1.181405e-09	-2.392873e-09	-1.094845e-08
2	-5.998677e-09	5.839759e-06	-1.774761e-08	-1.774761e-08	-5.884898e-09	5.839759e-06	5.839759e-06	-1.646201e-08	-1.646201e-08	1.904993e-06	...	-3.016425e-08	-1.253536e-08	-1.253536e-08	-3.088825e-08	-3.088825e-08	-9.431096e-09	-9.431096e-09	-4.656305e-09	-9.431096e-09	-4.315144e-08
3	-4.502953e-09	-1.774761e-08	3.290628e-06	3.290628e-06	-4.417544e-09	-1.774761e-08	-1.774761e-08	-1.235733e-08	-1.235733e-08	6.000546e-06	...	-2.264303e-08	-9.409764e-09	-9.409764e-09	-2.318650e-08	-2.318650e-08	-7.079525e-09	-7.079525e-09	-3.495291e-09	-7.079525e-09	-3.239196e-08
4	-4.502953e-09	-1.774761e-08	3.290628e-06	3.290628e-06	-4.417544e-09	-1.774761e-08	-1.774761e-08	-1.235733e-08	-1.235733e-08	6.000546e-06	...	-2.264303e-08	-9.409764e-09	-9.409764e-09	-2.318650e-08	-2.318650e-08	-7.079525e-09	-7.079525e-09	-3.495291e-09	-7.079525e-09	-3.239196e-08
5	-1.493126e-09	-5.884898e-09	-4.417544e-09	-4.417544e-09	3.618070e-07	-5.884898e-09	-5.884898e-09	-4.097546e-09	-4.097546e-09	3.347574e-07	...	-7.508161e-09	-3.120167e-09	-3.120167e-09	-7.688370e-09	-7.688370e-09	-2.347487e-09	-2.347487e-09	-1.158997e-09	-2.347487e-09	-1.074079e-08

5 rows × 29556 columns

COV.iloc[:5,:10].style.background_gradient()

	1	2	3	4	5	6	7	8	9	10
1	0.000000	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	0.000000
2	-0.000000	0.000006	-0.000000	-0.000000	-0.000000	0.000006	0.000006	-0.000000	-0.000000	0.000002
3	-0.000000	-0.000000	0.000003	0.000003	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	0.000006
4	-0.000000	-0.000000	0.000003	0.000003	-0.000000	-0.000000	-0.000000	-0.000000	-0.000000	0.000006
5	-0.000000	-0.000000	-0.000000	-0.000000	0.000000	-0.000000	-0.000000	-0.000000	-0.000000	0.000000

Decompose the Matrix

There a at least three options to choose from. We go with SciPy's Hermitian Eigendecomposition \ method eigh(), since our covarience matrix is symmetric.

from scipy.linalg import eigh

%time eig_vals, eig_vecs = eigh(COV)

Wall time: 38min 7s

Convert eigen data to dataframes

TERM_IDX = COV.index

EIG_VEC = pd.DataFrame(eig_vecs, index=TERM_IDX, columns=TERM_IDX)

EIG_VAL = pd.DataFrame(eig_vals, index=TERM_IDX, columns=['eig_val'])
EIG_VAL.index.name = 'term_id'

EIG_VEC.iloc[:5, :10].style.background_gradient()

term_id	1	2	3	4	5	6	7	8	9	10
term_id
1	0.000190	-0.001031	-0.000304	-0.000409	-0.000115	-0.000882	0.000172	-0.000482	0.000705	0.000537
2	0.050576	-0.001631	-0.017232	0.050186	-0.004421	-0.006207	-0.016342	-0.041056	0.080598	0.024476
3	-0.251753	-0.438206	-0.160220	-0.297737	0.218325	-0.217058	0.075961	0.031302	0.078241	0.029414
4	0.527026	-0.456357	0.027362	0.023019	-0.124215	0.060804	-0.069133	0.047233	-0.110051	-0.025309
5	-0.508285	-0.242777	0.262401	0.130595	-0.323529	-0.142930	-0.171910	-0.129455	0.028169	0.015121

EIG_VAL.iloc[:5]

	eig_val
term_id
1	-1.677703e-16
2	-1.053346e-16
3	-7.552308e-17
4	-6.765879e-17
5	-6.184650e-17

Select Principal Components

Next, we associate each eigenvalue with its corresponding column in the eigenvalue matrix. \ This is why we transpose the EIG_VEC dataframe.

Combine eigenvalues and eignvectors

EIG_PAIRS = EIG_VAL.join(EIG_VEC.T)

EIG_PAIRS.head()

	eig_val	1	2	3	4	5	6	7	8	9	...	29549	29550	29551	29552	29553	29554	29555	29556	29557	29558
term_id
1	-1.677703e-16	0.000190	0.050576	-0.251753	0.527026	-0.508285	0.428594	-0.239088	0.061406	-0.002834	...	0.000362	-0.000065	-0.000066	0.000383	-0.000073	0.000050	-0.000034	-0.000136	0.000084	0.000143
2	-1.053346e-16	-0.001031	-0.001631	-0.438206	-0.456357	-0.242777	0.159917	0.033440	-0.069839	-0.001033	...	0.001012	-0.000332	-0.000327	0.000570	0.000857	0.000218	0.000354	-0.000170	0.000242	0.000354
3	-7.552308e-17	-0.000304	-0.017232	-0.160220	0.027362	0.262401	-0.205032	-0.398608	0.293331	0.136964	...	0.000506	0.000581	0.000528	0.000188	0.000176	0.000148	0.000014	-0.000157	-0.000029	0.000796
4	-6.765879e-17	-0.000409	0.050186	-0.297737	0.023019	0.130595	-0.080832	-0.008371	0.244443	0.058025	...	0.000134	-0.000367	-0.000422	-0.000012	-0.000009	-0.000336	-0.000315	-0.000287	-0.000396	0.000387
5	-6.184650e-17	-0.000115	-0.004421	0.218325	-0.124215	-0.323529	-0.200387	-0.031553	0.464226	0.169672	...	-0.000131	-0.000459	-0.000463	-0.000195	-0.000245	0.000245	0.000235	0.000227	0.000307	-0.000142

5 rows × 29557 columns

Next, we sort in descending order and pick the top K (=10).

Compute and Show Explained Variance

We might have usd this value to sort our components.

EIG_PAIRS['exp_var'] = np.round((EIG_PAIRS.eig_val / EIG_PAIRS.eig_val.sum()) * 100, 2)

EIG_PAIRS.exp_var.sort_values(ascending=False).head().plot.bar(rot=45)

<matplotlib.axes._subplots.AxesSubplot at 0x247057f6630>

Pick Top K (10) Components

We pick these based on explained variance.

COMPS = EIG_PAIRS.sort_values('exp_var', ascending=False).head(10).reset_index(drop=True)
COMPS.index.name = 'comp_id'
COMPS.index = ["PC{}".format(i) for i in COMPS.index.tolist()]

COMPS

	eig_val	1	2	3	4	5	6	7	8	9	...	29550	29551	29552	29553	29554	29555	29556	29557	29558	exp_var
PC0	0.050096	0.000028	0.000069	0.000090	0.000090	3.489732e-05	0.000069	0.000069	0.000099	0.000099	...	0.000055	0.000055	0.000113	0.000113	0.000047	0.000047	0.000021	0.000047	0.000196	5.34
PC1	0.039452	-0.000052	-0.000191	-0.000168	-0.000168	-6.636143e-05	-0.000191	-0.000191	-0.000254	-0.000254	...	-0.000111	-0.000111	-0.000251	-0.000251	-0.000088	-0.000088	-0.000046	-0.000088	-0.000404	4.20
PC2	0.036098	0.000009	0.000035	0.000041	0.000041	2.256769e-05	0.000035	0.000035	0.000132	0.000132	...	0.000021	0.000021	0.000029	0.000029	0.000032	0.000032	0.000015	0.000032	0.000085	3.85
PC3	0.029467	0.000154	0.000548	0.000495	0.000495	2.016357e-04	0.000548	0.000548	0.000450	0.000450	...	0.000356	0.000356	0.000590	0.000590	0.000259	0.000259	0.000134	0.000259	0.001111	3.14
PC4	0.026793	-0.000068	-0.000234	-0.000228	-0.000228	-9.533447e-05	-0.000234	-0.000234	-0.000326	-0.000326	...	-0.000148	-0.000148	-0.000347	-0.000347	-0.000135	-0.000135	-0.000068	-0.000135	-0.000550	2.85
PC5	0.023343	-0.000327	-0.001058	-0.001078	-0.001078	-4.862476e-04	-0.001058	-0.001058	-0.000743	-0.000743	...	-0.000743	-0.000743	-0.001283	-0.001283	-0.000592	-0.000592	-0.000307	-0.000592	-0.002542	2.49
PC6	0.015340	0.000129	0.000281	0.000285	0.000285	2.311249e-04	0.000281	0.000281	-0.006631	-0.006631	...	0.000211	0.000211	0.000329	0.000329	0.000251	0.000251	0.000130	0.000251	0.001117	1.63
PC7	0.012568	-0.000085	-0.000046	-0.000210	-0.000210	-1.387821e-04	-0.000046	-0.000046	0.000178	0.000178	...	-0.000198	-0.000198	-0.000039	-0.000039	-0.000111	-0.000111	-0.000065	-0.000111	-0.000523	1.34
PC8	0.010517	0.000015	-0.000148	-0.000081	-0.000081	-1.883173e-07	-0.000148	-0.000148	-0.000195	-0.000195	...	0.000070	0.000070	-0.000136	-0.000136	-0.000040	-0.000040	-0.000002	-0.000040	-0.000140	1.12
PC9	0.008898	-0.000112	0.000031	-0.000189	-0.000189	-1.880461e-04	0.000031	0.000031	-0.000031	-0.000031	...	-0.000292	-0.000292	-0.000127	-0.000127	-0.000133	-0.000133	-0.000148	-0.000133	-0.000351	0.95

10 rows × 29558 columns

Inspect terms associated with eigenvectors

VOCAB.loc[[int(x) for x in EIG_PAIRS.sort_values('exp_var', ascending=False).head(10).index], 'term_str']

term_id
29558          être
29557           été
29556       émeutes
29555         élite
29554      échapper
29553    æstheticus
29552      æschylus
29551    ærostation
29550      æronauts
29549       æronaut
Name: term_str, dtype: object

Show Loadings

Loadings sow the contribution of each term to the component. \ We'll just look at the topi 10 words for the first two components in the Book version.

LOADINGS = COMPS[TERM_IDX].T
LOADINGS.index.name = 'term_id'

LOADINGS.head().style.background_gradient()

	PC0	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
term_id
1	0.000028	-0.000052	0.000009	0.000154	-0.000068	-0.000327	0.000129	-0.000085	0.000015	-0.000112
2	0.000069	-0.000191	0.000035	0.000548	-0.000234	-0.001058	0.000281	-0.000046	-0.000148	0.000031
3	0.000090	-0.000168	0.000041	0.000495	-0.000228	-0.001078	0.000285	-0.000210	-0.000081	-0.000189
4	0.000090	-0.000168	0.000041	0.000495	-0.000228	-0.001078	0.000285	-0.000210	-0.000081	-0.000189
5	0.000035	-0.000066	0.000023	0.000202	-0.000095	-0.000486	0.000231	-0.000139	-0.000000	-0.000188

LOADINGS['term_str'] = LOADINGS.apply(lambda x: VOCAB.loc[int(x.name)].term_str, 1)

l0_pos = LOADINGS.sort_values('PC0', ascending=True).head(10).term_str.str.cat(sep=' ')
l0_neg = LOADINGS.sort_values('PC0', ascending=False).head(10).term_str.str.cat(sep=' ')
l1_pos = LOADINGS.sort_values('PC1', ascending=True).head(10).term_str.str.cat(sep=' ')
l1_neg = LOADINGS.sort_values('PC1', ascending=False).head(10).term_str.str.cat(sep=' ')

print('Books PC0+', l0_pos)
print('Books PC0-', l0_neg)
print('Books PC1+', l1_pos)
print('Books PC1-', l1_neg)

Books PC0+ polly tom fanny maud fan shaw trix sydney grandma nt
Books PC0- jo ben meg dan rose mac laurie beth phebe bab
Books PC1+ ben bab celia thorny betty mac rose phebe sancho sanch
Books PC1- jo meg laurie dan beth amy march nat bhaer demi

Project Docs onto New Subspace

We get the dot product of the DTM matrix and the new component matrix, which we will call DCM -- for document-component matrix. \ This has the effect of replacing the features of the DTM with the features of the transposed component matrix.

DCM = TFIDF.dot(COMPS[TERM_IDX].T)

DCM

		PC0	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
book_id	chap_num
1	1	0.045586	-0.068268	0.021243	0.154930	-0.066606	-0.295966	0.092448	-0.045482	-0.000052	-0.043629
	2	0.036468	-0.052018	0.007954	0.116133	-0.046357	-0.195441	0.050429	-0.027351	0.004047	-0.025503
	3	0.031147	-0.054016	0.006790	0.103811	-0.048130	-0.174429	0.038767	-0.013583	-0.005808	-0.001074
	4	0.038437	-0.061656	0.016392	0.134503	-0.061723	-0.256758	0.085044	-0.032434	0.002938	-0.054203
	5	0.034846	-0.060136	0.017363	0.130076	-0.060269	-0.236491	0.065547	-0.026885	-0.000811	-0.043410
...	...	...	...	...	...	...	...	...	...	...	...
3795	20	0.099089	-0.425193	-0.435227	-0.217947	0.045435	0.081270	0.018139	-0.001406	0.001315	0.030075
	21	0.074833	-0.294456	-0.290467	-0.129541	0.024497	0.043422	0.025105	0.004605	0.004458	0.018747
	22	0.086546	-0.360335	-0.361546	-0.169126	0.033315	0.056944	0.015199	-0.011090	-0.005427	-0.012233
	23	0.103214	-0.451264	-0.475770	-0.251713	0.058225	0.107191	0.009404	-0.011621	-0.007712	0.014169
	24	0.056451	-0.178924	-0.106545	0.053196	-0.053017	-0.164683	-0.932541	0.018355	-0.024794	-0.007313

248 rows × 10 columns

We add metadata to our new, reduced matrices for display purposes.

DCM = DCM.join(LIB[['author','title']], on='book_id')

DCM['doc'] = DCM.apply(lambda x: "{}-{}-{}".format(x.author, x.title, x.name[1]), 1)

DCM.head().style.background_gradient()

		PC0	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	author	title	doc
book_id	chap_num
1	1	0.045586	-0.068268	0.021243	0.154930	-0.066606	-0.295966	0.092448	-0.045482	-0.000052	-0.043629	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-1
	2	0.036468	-0.052018	0.007954	0.116133	-0.046357	-0.195441	0.050429	-0.027351	0.004047	-0.025503	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-2
	3	0.031147	-0.054016	0.006790	0.103811	-0.048130	-0.174429	0.038767	-0.013583	-0.005808	-0.001074	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-3
	4	0.038437	-0.061656	0.016392	0.134503	-0.061723	-0.256758	0.085044	-0.032434	0.002938	-0.054203	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-4
	5	0.034846	-0.060136	0.017363	0.130076	-0.060269	-0.236491	0.065547	-0.026885	-0.000811	-0.043410	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-5

Visualize

def vis_pcs(M, a, b, label='author', prefix='PC'):
    fig = px.scatter(M, prefix + str(a), prefix + str(b), 
                        color=label, 
                        hover_name='doc', marginal_x='box')
    fig.show()

PC 0 and 1

vis_pcs(DCM, 0, 1)

vis_pcs(DCM, 0, 1, label='title')

PC 1 and 2

vis_pcs(DCM, 1, 2)

vis_pcs(DCM, 1, 2, label='title')

PC 2 and 3

vis_pcs(DCM, 2, 3)

vis_pcs(DCM, 2, 3, label='title')

PC 3 and 4

vis_pcs(DCM, 3, 4, label='author')

vis_pcs(DCM, 3, 4, label='title')

Compare to Scikit Learn

pca_engine = PCA(n_components=10)

DCM_sk = pd.DataFrame(pca_engine.fit_transform(TFIDF), index=TFIDF.index)
DCM_sk.columns = ['PC{}'.format(i) for i in DCM_sk.columns]
DCM_sk = DCM_sk.join(LIB[['author','title']], on='book_id')
DCM_sk['doc'] = DCM_sk.apply(lambda x: "{}-{}-{}".format(x.author, x.title, x.name[1]), 1)

DCM_sk.head().style.background_gradient()

		PC0	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	author	title	doc
book_id	chap_num
1	1	-0.045585	-0.068268	0.021243	0.154931	-0.066607	-0.295969	-0.092394	-0.046224	-0.001190	-0.049722	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-1
	2	-0.036468	-0.052018	0.007954	0.116133	-0.046357	-0.195443	-0.050399	-0.027435	0.002127	-0.025961	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-2
	3	-0.031147	-0.054016	0.006790	0.103810	-0.048132	-0.174428	-0.038739	-0.014089	-0.003805	-0.005026	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-3
	4	-0.038438	-0.061656	0.016393	0.134507	-0.061720	-0.256770	-0.084901	-0.031977	-0.001425	-0.065950	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-4
	5	-0.034846	-0.060136	0.017363	0.130080	-0.060267	-0.236501	-0.065406	-0.026532	-0.004682	-0.054766	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition	Poe-The Works of Edgar Allan Poe Volume 1 (of 5) of the Raven Edition-5

PC 0 and 1

vis_pcs(DCM_sk, 0, 1)

PC 0 and 1

vis_pcs(DCM_sk, 0, 1)

# px.scatter_3d(DCM_sk, 'PC0', 'PC1','PC2', color='title', hover_name='doc', height=1000, width=1200)

Show Loadings

LOADINGS_sk = pd.DataFrame(pca_engine.components_.T * np.sqrt(pca_engine.explained_variance_))
LOADINGS_sk.columns = ["PC{}".format(i) for i in LOADINGS_sk.columns]

LOADINGS_sk.index = TFIDF.columns
LOADINGS_sk.index.name = 'term_id'
LOADINGS_sk['term_str'] = LOADINGS_sk.apply(lambda x: VOCAB.loc[int(x.name)].term_str, 1)

pc0_pos = LOADINGS_sk.sort_values('PC0', ascending=False).head(10).term_str.str.cat(sep=' ')
pc0_neg = LOADINGS_sk.sort_values('PC0', ascending=True).head(10).term_str.str.cat(sep=' ')
pc1_pos = LOADINGS_sk.sort_values('PC1', ascending=False).head(10).term_str.str.cat(sep=' ')
pc1_neg = LOADINGS_sk.sort_values('PC1', ascending=True).head(10).term_str.str.cat(sep=' ')

print('BOOKS PC0+', pc0_pos)
print('BOOKS PC0-', pc0_neg)
print('BOOKS PC1+', pc1_pos)
print('BOOKS PC1-', pc1_neg)

BOOKS PC0+ polly tom fanny maud fan shaw trix sydney grandma nt
BOOKS PC0- jo ben meg dan rose mac laurie beth phebe bab
BOOKS PC1+ jo meg laurie dan beth amy march nat bhaer demi
BOOKS PC1- ben bab celia thorny betty mac rose phebe sancho sanch

SAVE

DCM.to_csv('PCA_DCM_chaps.csv')
COMPS.to_csv('PCA_TCM_chaps.csv')
DCM.to_csv('PCA_DCM_books.csv')
COMPS.to_csv('PCA_TCM_books.csv')

Topic Models (LDA)

n_terms = 4000
n_topics = 30
# raf - do minimum of 20 
max_iter = 5
OHCO = ['book_id', 'chap_num', 'para_num']

Imports

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation as LDA

%matplotlib inline

Prepare Data

Convert TOKENS to table of paragraphs

Scikit Learn wants an F1 style corpus. We create onefrom our annotated TOKEN table, keeping only regular nouns.

TOKENS = pd.read_csv('TOKEN2.csv')

TOKENS.head()

	book_id	chap_num	para_num	sent_num	token_num	pos_tuple	pos	token_str	term_str	term_id
0	2787	1	1	0	1	('IT', 'NNP')	NNP	IT	it	14145
1	2787	1	1	0	2	("'S", 'POS')	POS	'S	s	22463
2	2787	1	1	0	3	('time', 'NN')	NN	time	time	26439
3	2787	1	1	0	4	('to', 'TO')	TO	to	to	26510
4	2787	1	1	0	5	('go', 'VB')	VB	go	go	11429

PARAS = TOKENS[TOKENS.pos.str.match(r'^NNS?$')]\
    .groupby(OHCO).term_str\
    .apply(lambda x: ' '.join(x))\
    .to_frame()\
    .rename(columns={'term_str':'para_str'})

PARAS.head()

			para_str
book_id	chap_num	para_num
1	1	1	accounts city state excitement phenomena natur...
		2	day date crowd people purposes square city day...
		3	origin bulk masses area space queer substance ...
		4	meantime city object curiosity cause smoke min...
		5	balloon doubt feet earth crowd view person occ...

Create Vector Space

We use Scikit Learn's CountVectorizer to convert our F1 corpus of paragraphs into a document-term vector space of word counts.

tfv = CountVectorizer(max_features=n_terms, stop_words='english')
tf = tfv.fit_transform(PARAS.para_str)
TERMS = tfv.get_feature_names()

Generate Model

We run Scikit Learn's LatentDirichletAllocation algorithm and extract the THETA and PHI tables.

lda = LDA(n_components=n_topics, max_iter=max_iter, learning_offset=50., random_state=0)

THETA

THETA = pd.DataFrame(lda.fit_transform(tf), index=PARAS.index)
THETA.columns.name = 'topic_id'

THETA.sample(20).style.background_gradient()

		topic_id	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29
book_id	chap_num	para_num
2786	14	4	0.008333	0.008333	0.008333	0.008333	0.240102	0.008333	0.008333	0.008333	0.008333	0.008333	0.526564	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333	0.008333
	9	54	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.516667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667
2726	9	88	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.001667	0.951667	0.001667
514	9	73	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.516667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667
	21	91	0.211616	0.004167	0.004167	0.004167	0.004167	0.004167	0.544029	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.131855	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167	0.004167
2804	24	20	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.528560	0.003030	0.003030	0.003030	0.003030	0.200323	0.189299	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030
2786	21	40	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.677778	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111
514	25	29	0.002222	0.002222	0.002222	0.356931	0.580846	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222	0.002222
2726	21	4	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.685035	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333	0.123445	0.101520	0.003333	0.003333	0.003333	0.003333	0.003333	0.003333
5	2	25	0.011111	0.011111	0.011111	0.011111	0.407977	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.280911	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111
2787	16	12	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.592953	0.006667	0.006667	0.006667	0.006667	0.220380	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667
5	1	3	0.001852	0.001852	0.001852	0.001852	0.001852	0.328877	0.001852	0.001852	0.001852	0.201613	0.001852	0.001852	0.419510	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852	0.001852
3795	15	61	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.590653	0.006667	0.006667	0.006667	0.006667	0.222680	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667	0.006667
2726	15	83	0.341400	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.176786	0.005556	0.005556	0.005556	0.005556	0.331814	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556	0.005556
	18	59	0.004762	0.338861	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.527805	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762	0.004762
1	2	9	0.003030	0.003030	0.666727	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.248424	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030	0.003030
2787	15	94	0.003704	0.156217	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.502373	0.003704	0.003704	0.241410	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704	0.003704
	18	40	0.011111	0.011111	0.677778	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111	0.011111
2804	23	14	0.002564	0.002564	0.002564	0.450859	0.002564	0.002564	0.002564	0.002564	0.061532	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.418378	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564	0.002564
3795	8	34	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.516667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667	0.016667

PHI

PHI = pd.DataFrame(lda.components_, columns=TERMS)
PHI.index.name = 'topic_id'
PHI.columns.name  = 'term_str'

PHI.T.head().style.background_gradient()

topic_id	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29
term_str
aback	1.033333	0.033333	0.251742	0.033333	0.033333	0.033333	0.814925	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	4.033333	1.033333	2.033333	0.033333	0.033333	0.033333	0.033333	0.033333	2.033333	0.033333	0.033333	0.033333
ability	2.033287	0.093171	4.890068	0.033333	0.033333	3.265507	0.033333	1.859361	0.033333	1.008706	0.033333	1.033333	0.354354	1.633178	0.033333	0.033334	0.033333	0.033333	0.033333	2.802126	0.104133	0.033333	0.033333	0.033333	0.033333	5.322708	0.033399	0.033334	0.033333	0.033333
absence	2.112087	9.632519	3.307857	0.033333	1.237051	6.487703	4.407342	0.639597	2.732606	5.765650	1.890409	0.033333	1.128253	0.033333	0.194323	0.424132	0.794770	0.033333	2.843909	0.039627	4.754014	0.446947	0.033333	0.033333	0.033333	2.324727	0.459613	0.620334	10.201389	3.321809
absent	1.033333	0.033333	1.512178	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.909590	2.008445	0.033333	0.579377	0.033333	0.033333	0.033333	1.157076	0.033333	4.033333
absorbed	0.033333	0.033333	0.033333	0.033333	2.026674	0.033333	0.033333	0.033333	0.033333	0.033333	0.033333	0.767203	0.033333	0.033333	0.033333	1.211021	0.033333	0.033333	0.033333	0.033333	0.033333	2.034619	0.033333	1.033333	0.033333	0.033333	0.033333	0.033333	0.033333	1.127149

Inspect Results

Get Top Terms per Topic

TOPICS = PHI.stack().to_frame().rename(columns={0:'weight'})\
    .groupby('topic_id')\
    .apply(lambda x: 
           x.weight.sort_values(ascending=False)\
               .head(10)\
               .reset_index()\
               .drop('topic_id',1)\
               .term_str)

TOPICS

term_str	0	1	2	3	4	5	6	7	8	9
topic_id
0	chance	idea	corner	dog	thing	things	face	smile	eye	fun
1	life	word	mind	letter	letters	flowers	boy	friends	face	things
2	uncle	time	piece	manner	hand	board	fact	length	box	thing
3	arm	breath	course	window	round	home	head	eyes	queer	face
4	eyes	face	hand	look	heart	hands	voice	words	tender	kind
5	eyes	question	man	head	happiness	life	sake	face	cause	love
6	way	heart	life	music	time	pleasure	sort	world	day	father
7	head	laughter	face	thing	boys	work	places	effect	girls	steps
8	man	tone	fellow	head	book	words	minute	business	eye	look
9	beauty	days	scene	man	style	roof	feet	articles	object	girls
10	sir	air	laugh	turn	joke	answer	lady	time	song	people
11	heart	face	thank	thing	boy	hands	girls	man	satisfaction	troubles
12	day	time	thing	child	money	girl	spite	boy	girls	eyes
13	dear	trouble	time	people	duty	game	wits	things	order	home
14	boys	boy	girls	children	time	ladies	things	water	boat	end
15	surprise	week	way	love	child	hurry	course	day	everybody	heart
16	hair	dozen	times	news	information	face	clothes	thing	sound	door
17	death	nature	idea	length	feelings	reason	fancy	glance	time	horror
18	wall	reply	time	hill	till	bird	eyes	air	stage	legs
19	thing	feet	earth	time	balloon	miles	surface	world	man	distance
20	work	tm	works	terms	money	people	things	maam	agreement	ways
21	room	things	ones	chair	day	house	brother	door	way	boys
22	story	year	place	school	home	boys	house	children	time	years
23	mother	home	father	morning	way	girls	bread	glad	country	folks
24	things	dress	hand	paper	silk	king	gloves	leaves	sort	child
25	matter	man	things	home	boys	law	parlor	ha	time	cousin
26	door	box	moon	foot	doors	room	gold	drawer	portion	walls
27	girl	boys	night	thing	way	lady	time	pardon	mother	nod
28	voice	body	hour	day	night	corpse	river	opinion	words	man
29	time	bit	man	head	gentleman	word	lot	love	heart	right

TOPICS['label'] = TOPICS.apply(lambda x: str(x.name) + ' ' + ' '.join(x), 1)

Sort Topics by Doc Weight

TOPICS['doc_weight_sum'] = THETA.sum()

TOPICS.sort_values('doc_weight_sum', ascending=True).plot.barh(y='doc_weight_sum', x='label', figsize=(5,10)) 

<matplotlib.axes._subplots.AxesSubplot at 0x24709068278>

Explore Topics by Author

LIB = pd.read_csv('LIB3.csv').set_index('book_id')

topic_cols = [t for t in range(n_topics)]
AUTHORS = THETA.join(LIB, on='book_id')\
    .reset_index().set_index(['author']+OHCO)\
    .groupby('author')[topic_cols].mean()\
    .T                                            
AUTHORS.index.name = 'topic_id'

AUTHORS['topterms'] = TOPICS[[i for i in range(10)]].apply(lambda x: ' '.join(x), 1)

AUTHORS.sort_values('Alcott', ascending=False).style.background_gradient()

author	Alcott	Poe	topterms
topic_id
4	0.058236	0.026527	eyes face hand look heart hands voice words tender kind
14	0.051595	0.019103	boys boy girls children time ladies things water boat end
21	0.044982	0.016925	room things ones chair day house brother door way boys
1	0.044380	0.027477	life word mind letter letters flowers boy friends face things
23	0.043846	0.017903	mother home father morning way girls bread glad country folks
12	0.043372	0.021972	day time thing child money girl spite boy girls eyes
6	0.042088	0.025352	way heart life music time pleasure sort world day father
24	0.041513	0.024015	things dress hand paper silk king gloves leaves sort child
0	0.037182	0.023251	chance idea corner dog thing things face smile eye fun
27	0.036441	0.020809	girl boys night thing way lady time pardon mother nod
22	0.036102	0.024793	story year place school home boys house children time years
8	0.036012	0.048340	man tone fellow head book words minute business eye look
29	0.035843	0.035062	time bit man head gentleman word lot love heart right
10	0.033345	0.030228	sir air laugh turn joke answer lady time song people
13	0.032951	0.021045	dear trouble time people duty game wits things order home
16	0.032884	0.019800	hair dozen times news information face clothes thing sound door
3	0.032830	0.022840	arm breath course window round home head eyes queer face
15	0.032391	0.029738	surprise week way love child hurry course day everybody heart
20	0.032214	0.023679	work tm works terms money people things maam agreement ways
11	0.032179	0.024309	heart face thank thing boy hands girls man satisfaction troubles
18	0.029091	0.032364	wall reply time hill till bird eyes air stage legs
7	0.029053	0.023526	head laughter face thing boys work places effect girls steps
25	0.023863	0.040609	matter man things home boys law parlor ha time cousin
19	0.021997	0.056067	thing feet earth time balloon miles surface world man distance
26	0.021389	0.042705	door box moon foot doors room gold drawer portion walls
28	0.020691	0.071338	voice body hour day night corpse river opinion words man
5	0.019059	0.056162	eyes question man head happiness life sake face cause love
2	0.018633	0.065651	uncle time piece manner hand board fact length box thing
17	0.018551	0.061727	death nature idea length feelings reason fancy glance time horror
9	0.017287	0.046684	beauty days scene man style roof feet articles object girls

AUTHORS.sort_values('Poe', ascending=False).style.background_gradient()

author	Alcott	Poe	topterms
topic_id
28	0.020691	0.071338	voice body hour day night corpse river opinion words man
2	0.018633	0.065651	uncle time piece manner hand board fact length box thing
17	0.018551	0.061727	death nature idea length feelings reason fancy glance time horror
5	0.019059	0.056162	eyes question man head happiness life sake face cause love
19	0.021997	0.056067	thing feet earth time balloon miles surface world man distance
8	0.036012	0.048340	man tone fellow head book words minute business eye look
9	0.017287	0.046684	beauty days scene man style roof feet articles object girls
26	0.021389	0.042705	door box moon foot doors room gold drawer portion walls
25	0.023863	0.040609	matter man things home boys law parlor ha time cousin
29	0.035843	0.035062	time bit man head gentleman word lot love heart right
18	0.029091	0.032364	wall reply time hill till bird eyes air stage legs
10	0.033345	0.030228	sir air laugh turn joke answer lady time song people
15	0.032391	0.029738	surprise week way love child hurry course day everybody heart
1	0.044380	0.027477	life word mind letter letters flowers boy friends face things
4	0.058236	0.026527	eyes face hand look heart hands voice words tender kind
6	0.042088	0.025352	way heart life music time pleasure sort world day father
22	0.036102	0.024793	story year place school home boys house children time years
11	0.032179	0.024309	heart face thank thing boy hands girls man satisfaction troubles
24	0.041513	0.024015	things dress hand paper silk king gloves leaves sort child
20	0.032214	0.023679	work tm works terms money people things maam agreement ways
7	0.029053	0.023526	head laughter face thing boys work places effect girls steps
0	0.037182	0.023251	chance idea corner dog thing things face smile eye fun
3	0.032830	0.022840	arm breath course window round home head eyes queer face
12	0.043372	0.021972	day time thing child money girl spite boy girls eyes
13	0.032951	0.021045	dear trouble time people duty game wits things order home
27	0.036441	0.020809	girl boys night thing way lady time pardon mother nod
16	0.032884	0.019800	hair dozen times news information face clothes thing sound door
14	0.051595	0.019103	boys boy girls children time ladies things water boat end
23	0.043846	0.017903	mother home father morning way girls bread glad country folks
21	0.044982	0.016925	room things ones chair day house brother door way boys

import plotly_express as px

# px.scatter(AUTHORS.reset_index(), 'Alcott', 'melville', hover_name='topterms', text='topic_id')\
#     .update_traces(mode='text')

Clutser Topics

import scipy.cluster.hierarchy as sch
from scipy.spatial.distance import pdist
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import normalize
import matplotlib.pyplot as plt

def plot_tree(tree, labels):
    plt.figure()
    fig, axes = plt.subplots(figsize=(5, 10))
    dendrogram = sch.dendrogram(tree, labels=labels, orientation="left")
    plt.tick_params(axis='both', which='major', labelsize=14)

SIMS = pdist(normalize(PHI), metric='euclidean')
TREE = sch.linkage(SIMS, method='ward')

labels  = ["{}: {}".format(a,b) for a, b in zip(AUTHORS.index,  AUTHORS.topterms.tolist())]

plot_tree(TREE, labels)

<Figure size 432x288 with 0 Axes>

LDA Viz

import pyLDAvis
import pyLDAvis.sklearn
pyLDAvis.enable_notebook()

pyLDAvis.sklearn.prepare(lda, tf, tfv)

Word Embeddings (word2vec)

OHCO = ['book_id', 'chap_num', 'para_num', 'sent_num', 'token_num']
BAG = OHCO[:4] # Paragraphs
# BAG = OHCO[:5] # Sentences
window = 5

Imports

from gensim.models import word2vec
from sklearn.manifold import TSNE
import plotly_express as px

C:\ProgramData\Anaconda3\lib\site-packages\gensim\corpora\dictionary.py:11: DeprecationWarning:

Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working

C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse\sparsetools.py:21: DeprecationWarning:

`scipy.sparse.sparsetools` is deprecated!
scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.

%matplotlib inline

Process

Import TOKENS and convert to a corpus for Gensim

We import data from the TOKEN table of the novels corpus, excluding proper nouns.

TOKENS = pd.read_csv('TOKEN2.csv').set_index(OHCO)

corpus = TOKENS[~TOKENS.pos.str.match('NNPS?')]\
    .groupby(BAG)\
    .term_str.apply(lambda  x:  x.tolist())\
    .reset_index()['term_str'].tolist()

corpus

[['the'],
 ['late',
  'accounts',
  'from',
  'that',
  'city',
  'seems',
  'to',
  'be',
  'in',
  'a',
  'high',
  'state',
  'of',
  'philosophical',
  'excitement'],
 ['indeed',
  'phenomena',
  'have',
  'there',
  'occurred',
  'of',
  'a',
  'nature',
  'so',
  'completely',
  'unexpected',
  'so',
  'entirely',
  'novel',
  'so',
  'utterly',
  'at',
  'variance',
  'with',
  'preconceived',
  'opinions',
  'as',
  'to',
  'leave',
  'no',
  'doubt',
  'on',
  'my',
  'mind',
  'that',
  'long',
  'ere',
  'this',
  'all',
  'is',
  'in',
  'an',
  'uproar',
  'all',
  'physics',
  'in',
  'a',
  'ferment',
  'all',
  'reason',
  'and',
  'astronomy',
  'together',
  'by',
  'the',
  'ears'],
 ['it',
  'appears',
  'that',
  'on',
  'the',
  'day',
  'of',
  'i',
  'am',
  'not',
  'positive',
  'about',
  'the',
  'date',
  'a',
  'vast',
  'crowd',
  'of',
  'people',
  'for',
  'purposes',
  'not',
  'specifically',
  'mentioned',
  'were',
  'assembled',
  'in',
  'the',
  'great',
  'square',
  'of',
  'the',
  'in',
  'the',
  'wellconditioned',
  'city',
  'of'],
 ['the',
  'day',
  'was',
  'warm',
  'unusually',
  'so',
  'for',
  'the',
  'season',
  'there',
  'was',
  'hardly',
  'a',
  'breath',
  'of',
  'air',
  'stirring',
  'and',
  'the',
  'multitude',
  'were',
  'in',
  'no',
  'bad',
  'humor',
  'at',
  'being',
  'now',
  'and',
  'then',
  'besprinkled',
  'with',
  'friendly',
  'showers',
  'of',
  'momentary',
  'duration',
  'that',
  'fell',
  'from',
  'large',
  'white',
  'masses',
  'of',
  'cloud',
  'which',
  'chequered',
  'in',
  'a',
  'fitful',
  'manner',
  'the',
  'blue',
  'vault',
  'of',
  'the',
  'firmament'],
 ['nevertheless',
  'about',
  'noon',
  'a',
  'slight',
  'but',
  'remarkable',
  'agitation',
  'became',
  'apparent',
  'in',
  'the',
  'assembly',
  'the',
  'clattering',
  'of',
  'ten',
  'thousand',
  'tongues',
  'succeeded',
  'and',
  'in',
  'an',
  'instant',
  'afterward',
  'ten',
  'thousand',
  'faces',
  'were',
  'upturned',
  'toward',
  'the',
  'heavens',
  'ten',
  'thousand',
  'pipes',
  'descended',
  'simultaneously',
  'from',
  'the',
  'corners',
  'of',
  'ten',
  'thousand',
  'mouths',
  'and',
  'a',
  'shout',
  'which',
  'could',
  'be',
  'compared',
  'to',
  'nothing',
  'but',
  'the',
  'roaring',
  'of',
  'resounded',
  'long',
  'loudly',
  'and',
  'furiously',
  'through',
  'all',
  'the',
  'environs',
  'of'],
 ['the',
  'origin',
  'of',
  'this',
  'hubbub',
  'soon',
  'became',
  'sufficiently',
  'evident'],
 ['from',
  'behind',
  'the',
  'huge',
  'bulk',
  'of',
  'one',
  'of',
  'those',
  'sharplydefined',
  'masses',
  'of',
  'cloud',
  'already',
  'mentioned',
  'was',
  'seen',
  'slowly',
  'to',
  'emerge',
  'into',
  'an',
  'open',
  'area',
  'of',
  'blue',
  'space',
  'a',
  'queer',
  'heterogeneous',
  'but',
  'apparently',
  'solid',
  'substance',
  'so',
  'oddly',
  'shaped',
  'so',
  'whimsically',
  'put',
  'together',
  'as',
  'not',
  'to',
  'be',
  'in',
  'any',
  'manner',
  'comprehended',
  'and',
  'never',
  'to',
  'be',
  'sufficiently',
  'admired',
  'by',
  'the',
  'host',
  'of',
  'sturdy',
  'burghers',
  'who',
  'stood',
  'openmouthed',
  'below'],
 ['what', 'could', 'it', 'be'],
 ['in',
  'the',
  'name',
  'of',
  'all',
  'the',
  'vrows',
  'and',
  'devils',
  'in',
  'what',
  'could',
  'it',
  'possibly',
  'portend'],
 ['no',
  'one',
  'knew',
  'no',
  'one',
  'could',
  'imagine',
  'no',
  'one',
  'not',
  'even',
  'the',
  'burgomaster',
  'had',
  'the',
  'slightest',
  'clew',
  'by',
  'which',
  'to',
  'unravel',
  'the',
  'mystery',
  'so',
  'as',
  'nothing',
  'more',
  'reasonable',
  'could',
  'be',
  'done',
  'every',
  'one',
  'to',
  'a',
  'man',
  'replaced',
  'his',
  'pipe',
  'carefully',
  'in',
  'the',
  'corner',
  'of',
  'his',
  'mouth',
  'and',
  'cocking',
  'up',
  'his',
  'right',
  'eye',
  'towards',
  'the',
  'phenomenon',
  'puffed',
  'paused',
  'waddled',
  'about',
  'and',
  'grunted',
  'significantly',
  'then',
  'waddled',
  'back',
  'grunted',
  'paused',
  'and',
  'finally',
  'puffed',
  'again'],
 ['in',
  'the',
  'meantime',
  'however',
  'lower',
  'and',
  'still',
  'lower',
  'toward',
  'the',
  'goodly',
  'city',
  'came',
  'the',
  'object',
  'of',
  'so',
  'much',
  'curiosity',
  'and',
  'the',
  'cause',
  'of',
  'so',
  'much',
  'smoke'],
 ['in',
  'a',
  'very',
  'few',
  'minutes',
  'it',
  'arrived',
  'near',
  'enough',
  'to',
  'be',
  'accurately',
  'discerned'],
 ['it', 'appeared', 'to', 'be', 'yes'],
 ['it',
  'was',
  'undoubtedly',
  'a',
  'species',
  'of',
  'balloon',
  'but',
  'surely',
  'no',
  'such',
  'balloon',
  'had',
  'ever',
  'been',
  'seen',
  'in',
  'before'],
 ['for',
  'who',
  'let',
  'me',
  'ask',
  'ever',
  'heard',
  'of',
  'a',
  'balloon',
  'manufactured',
  'entirely',
  'of',
  'dirty',
  'newspapers'],
 ['no',
  'man',
  'in',
  'certainly',
  'yet',
  'here',
  'under',
  'the',
  'very',
  'noses',
  'of',
  'the',
  'people',
  'or',
  'rather',
  'at',
  'some',
  'distance',
  'above',
  'their',
  'noses',
  'was',
  'the',
  'identical',
  'thing',
  'in',
  'question',
  'and',
  'composed',
  'i',
  'have',
  'it',
  'on',
  'the',
  'best',
  'authority',
  'of',
  'the',
  'precise',
  'material',
  'which',
  'no',
  'one',
  'had',
  'ever',
  'before',
  'known',
  'to',
  'be',
  'used',
  'for',
  'a',
  'similar',
  'purpose'],
 ['it',
  'was',
  'an',
  'egregious',
  'insult',
  'to',
  'the',
  'good',
  'sense',
  'of',
  'the',
  'burghers',
  'of'],
 ['as',
  'to',
  'the',
  'shape',
  'of',
  'the',
  'phenomenon',
  'it',
  'was',
  'even',
  'still',
  'more',
  'reprehensible'],
 ['being',
  'little',
  'or',
  'nothing',
  'better',
  'than',
  'a',
  'huge',
  'foolscap',
  'turned',
  'upside',
  'down'],
 ['and',
  'this',
  'similitude',
  'was',
  'regarded',
  'as',
  'by',
  'no',
  'means',
  'lessened',
  'when',
  'upon',
  'nearer',
  'inspection',
  'there',
  'was',
  'perceived',
  'a',
  'large',
  'tassel',
  'depending',
  'from',
  'its',
  'apex',
  'and',
  'around',
  'the',
  'upper',
  'rim',
  'or',
  'base',
  'of',
  'the',
  'cone',
  'a',
  'circle',
  'of',
  'little',
  'instruments',
  'resembling',
  'sheepbells',
  'which',
  'kept',
  'up',
  'a',
  'continual',
  'tinkling',
  'to',
  'the',
  'tune',
  'of'],
 ['but', 'still', 'worse'],
 ['suspended',
  'by',
  'blue',
  'ribbons',
  'to',
  'the',
  'end',
  'of',
  'this',
  'fantastic',
  'machine',
  'there',
  'hung',
  'by',
  'way',
  'of',
  'car',
  'an',
  'enormous',
  'drab',
  'beaver',
  'hat',
  'with',
  'a',
  'brim',
  'superlatively',
  'broad',
  'and',
  'a',
  'hemispherical',
  'crown',
  'with',
  'a',
  'black',
  'band',
  'and',
  'a',
  'silver',
  'buckle'],
 ['it',
  'is',
  'however',
  'somewhat',
  'remarkable',
  'that',
  'many',
  'citizens',
  'of',
  'swore',
  'to',
  'having',
  'seen',
  'the',
  'same',
  'hat',
  'repeatedly',
  'before',
  'and',
  'indeed',
  'the',
  'whole',
  'assembly',
  'seemed',
  'to',
  'regard',
  'it',
  'with',
  'eyes',
  'of',
  'familiarity',
  'while',
  'the',
  'vrow',
  'upon',
  'sight',
  'of',
  'it',
  'uttered',
  'an',
  'exclamation',
  'of',
  'joyful',
  'surprise',
  'and',
  'declared',
  'it',
  'to',
  'be',
  'the',
  'identical',
  'hat',
  'of',
  'her',
  'good',
  'man',
  'himself'],
 ['now',
  'this',
  'was',
  'a',
  'circumstance',
  'the',
  'more',
  'to',
  'be',
  'observed',
  'as',
  'with',
  'three',
  'companions',
  'had',
  'actually',
  'disappeared',
  'from',
  'about',
  'five',
  'years',
  'before',
  'in',
  'a',
  'very',
  'sudden',
  'and',
  'unaccountable',
  'manner',
  'and',
  'up',
  'to',
  'the',
  'date',
  'of',
  'this',
  'narrative',
  'all',
  'attempts',
  'had',
  'failed',
  'of',
  'obtaining',
  'any',
  'intelligence',
  'concerning',
  'them',
  'whatsoever'],
 ['to',
  'be',
  'sure',
  'some',
  'bones',
  'which',
  'were',
  'thought',
  'to',
  'be',
  'human',
  'mixed',
  'up',
  'with',
  'a',
  'quantity',
  'of',
  'oddlooking',
  'rubbish',
  'had',
  'been',
  'lately',
  'discovered',
  'in',
  'a',
  'retired',
  'situation',
  'to',
  'the',
  'east',
  'of',
  'and',
  'some',
  'people',
  'went',
  'so',
  'far',
  'as',
  'to',
  'imagine',
  'that',
  'in',
  'this',
  'spot',
  'a',
  'foul',
  'murder',
  'had',
  'been',
  'committed',
  'and',
  'that',
  'the',
  'sufferers',
  'were',
  'in',
  'all',
  'probability',
  'and',
  'his',
  'associates'],
 ['but', 'to', 'return'],
 ['the',
  'balloon',
  'for',
  'such',
  'no',
  'doubt',
  'it',
  'was',
  'had',
  'now',
  'descended',
  'to',
  'within',
  'a',
  'hundred',
  'feet',
  'of',
  'the',
  'earth',
  'allowing',
  'the',
  'crowd',
  'below',
  'a',
  'sufficiently',
  'distinct',
  'view',
  'of',
  'the',
  'person',
  'of',
  'its',
  'occupant'],
 ['this', 'was', 'in', 'truth', 'a', 'very', 'droll', 'little', 'somebody'],
 ['he',
  'could',
  'not',
  'have',
  'been',
  'more',
  'than',
  'two',
  'feet',
  'in',
  'height',
  'but',
  'this',
  'altitude',
  'little',
  'as',
  'it',
  'was',
  'would',
  'have',
  'been',
  'sufficient',
  'to',
  'destroy',
  'his',
  'equilibrium',
  'and',
  'tilt',
  'him',
  'over',
  'the',
  'edge',
  'of',
  'his',
  'tiny',
  'car',
  'but',
  'for',
  'the',
  'intervention',
  'of',
  'a',
  'circular',
  'rim',
  'reaching',
  'as',
  'high',
  'as',
  'the',
  'breast',
  'and',
  'rigged',
  'on',
  'to',
  'the',
  'cords',
  'of',
  'the',
  'balloon'],
 ['the',
  'body',
  'of',
  'the',
  'little',
  'man',
  'was',
  'more',
  'than',
  'proportionately',
  'broad',
  'giving',
  'to',
  'his',
  'entire',
  'figure',
  'a',
  'rotundity',
  'highly',
  'absurd'],
 ['his',
  'feet',
  'of',
  'course',
  'could',
  'not',
  'be',
  'seen',
  'at',
  'all',
  'although',
  'a',
  'horny',
  'substance',
  'of',
  'suspicious',
  'nature',
  'was',
  'occasionally',
  'protruded',
  'through',
  'a',
  'rent',
  'in',
  'the',
  'bottom',
  'of',
  'the',
  'car',
  'or',
  'to',
  'speak',
  'more',
  'properly',
  'in',
  'the',
  'top',
  'of',
  'the',
  'hat'],
 ['his', 'hands', 'were', 'enormously', 'large'],
 ['his',
  'hair',
  'was',
  'extremely',
  'gray',
  'and',
  'collected',
  'in',
  'a',
  'cue',
  'behind'],
 ['his',
  'nose',
  'was',
  'prodigiously',
  'long',
  'crooked',
  'and',
  'inflammatory',
  'his',
  'eyes',
  'full',
  'brilliant',
  'and',
  'acute',
  'his',
  'chin',
  'and',
  'cheeks',
  'although',
  'wrinkled',
  'with',
  'age',
  'were',
  'broad',
  'puffy',
  'and',
  'double',
  'but',
  'of',
  'ears',
  'of',
  'any',
  'kind',
  'or',
  'character',
  'there',
  'was',
  'not',
  'a',
  'semblance',
  'to',
  'be',
  'discovered',
  'upon',
  'any',
  'portion',
  'of',
  'his',
  'head'],
 ['this',
  'odd',
  'little',
  'gentleman',
  'was',
  'dressed',
  'in',
  'a',
  'loose',
  'surtout',
  'of',
  'skyblue',
  'satin',
  'with',
  'tight',
  'breeches',
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  'fastened',
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  'silver',
  'buckles',
  'at',
  'the',
  'knees'],
 ['his',
  'vest',
  'was',
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  'some',
  'bright',
  'yellow',
  'material',
  'a',
  'white',
  'taffety',
  'cap',
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  'set',
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  'on',
  'one',
  'side',
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  'head',
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  'equipment',
  'a',
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  'handkerchief',
  'enveloped',
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  'throat',
  'and',
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  'a',
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  'manner',
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  'his',
  'bosom',
  'in',
  'a',
  'fantastic',
  'bowknot',
  'of',
  'supereminent',
  'dimensions'],
 ['having',
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  'said',
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  'to',
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  'one',
  'hundred',
  'feet',
  'from',
  'the',
  'surface',
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  'the',
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  'old',
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  'an',
  'angle',
  'of',
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  'sixtyfive',
  'seconds',
  'and',
  'whose',
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  'hue',
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  'intensity',
  'was',
  'at',
  'all',
  'times',
  'darker',
  'than',
  'any',
  'other',
  'spot',
  'upon',
  'the',
  'visible',
  'hemisphere',
  'and',
  'occasionally',
  'deepened',
  'into',
  'the',
  'most',
  'absolute',
  'and',
  'impenetrable',
  'blackness'],
 ['farther', 'than', 'this', 'little', 'could', 'be', 'ascertained'],
 ['by',
  'twelve',
  'o',
  'clock',
  'the',
  'circular',
  'centre',
  'had',
  'materially',
  'decreased',
  'in',
  'circumference',
  'and',
  'by',
  'seven'],
 ['i',
  'lost',
  'sight',
  'of',
  'it',
  'entirely',
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  'balloon',
  'passing',
  'over',
  'the',
  'western',
  'limb',
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  'the',
  'ice',
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  'floating',
  'away',
  'rapidly',
  'in',
  'the',
  'direction',
  'of',
  'the',
  'equator'],
 ['8th'],
 ['found',
  'a',
  'sensible',
  'diminution',
  'in',
  'the',
  'earth',
  's',
  'apparent',
  'diameter',
  'besides',
  'a',
  'material',
  'alteration',
  'in',
  'its',
  'general',
  'color',
  'and',
  'appearance'],
 ['the',
  'whole',
  'visible',
  'area',
  'partook',
  'in',
  'different',
  'degrees',
  'of',
  'a',
  'tint',
  'of',
  'pale',
  'yellow',
  'and',
  'in',
  'some',
  'portions',
  'had',
  'acquired',
  'a',
  'brilliancy',
  'even',
  'painful',
  'to',
  'the',
  'eye'],
 ['my',
  'view',
  'downward',
  'was',
  'also',
  'considerably',
  'impeded',
  'by',
  'the',
  'dense',
  'atmosphere',
  'in',
  'the',
  'vicinity',
  'of',
  'the',
  'surface',
  'being',
  'loaded',
  'with',
  'clouds',
  'between',
  'whose',
  'masses',
  'i',
  'could',
  'only',
  'now',
  'and',
  'then',
  'obtain',
  'a',
  'glimpse',
  'of',
  'the',
  'earth',
  'itself'],
 ['this',
  'difficulty',
  'of',
  'direct',
  'vision',
  'had',
  'troubled',
  'me',
  'more',
  'or',
  'less',
  'for',
  'the',
  'last',
  'fortyeight',
  'hours',
  'but',
  'my',
  'present',
  'enormous',
  'elevation',
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  'closer',
  'together',
  'as',
  'it',
  'were',
  'the',
  'floating',
  'bodies',
  'of',
  'vapor',
  'and',
  'the',
  'inconvenience',
  'became',
  'of',
  'course',
  'more',
  'and',
  'more',
  'palpable',
  'in',
  'proportion',
  'to',
  'my',
  'ascent'],
 ['nevertheless',
  'i',
  'could',
  'easily',
  'perceive',
  'that',
  'the',
  'balloon',
  'now',
  'hovered',
  'above',
  'the',
  'range',
  'of',
  'great',
  'lakes',
  'in',
  'the',
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  'of',
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  'holding',
  'a',
  'course',
  'due',
  'south',
  'which',
  'would',
  'bring',
  'me',
  'to',
  'the',
  'tropics'],
 ['this',
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  'did',
  'not',
  'fail',
  'to',
  'give',
  'me',
  'the',
  'most',
  'heartful',
  'satisfaction',
  'and',
  'i',
  'hailed',
  'it',
  'as',
  'a',
  'happy',
  'omen',
  'of',
  'ultimate',
  'success'],
 ['indeed',
  'the',
  'direction',
  'i',
  'had',
  'hitherto',
  'taken',
  'had',
  'filled',
  'me',
  'with',
  'uneasiness',
  'for',
  'it',
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  'evident',
  'that',
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  'i',
  'continued',
  'it',
  'much',
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  'there',
  'would',
  'have',
  'been',
  'no',
  'possibility',
  'of',
  'my',
  'arriving',
  'at',
  'the',
  'moon',
  'at',
  'all',
  'whose',
  'orbit',
  'is',
  'inclined',
  'to',
  'the',
  'ecliptic',
  'at',
  'only',
  'the',
  'small',
  'angle',
  'of',
  '5',
  'degrees',
  '8',
  '48'],
 ['9th'],
 ['today',
  'the',
  'earth',
  's',
  'diameter',
  'was',
  'greatly',
  'diminished',
  'and',
  'the',
  'color',
  'of',
  'the',
  'surface',
  'assumed',
  'hourly',
  'a',
  'deeper',
  'tint',
  'of',
  'yellow'],
 ['the',
  'balloon',
  'kept',
  'steadily',
  'on',
  'her',
  'course',
  'to',
  'the',
  'southward',
  'and',
  'arrived',
  'at',
  'nine',
  'over',
  'the',
  'northern',
  'edge',
  'of',
  'the'],
 ['10th'],
 ['i',
  'was',
  'suddenly',
  'aroused',
  'from',
  'slumber',
  'about',
  'five',
  'o',
  'clock',
  'this',
  'morning',
  'by',
  'a',
  'loud',
  'crackling',
  'and',
  'terrific',
  'sound',
  'for',
  'which',
  'i',
  'could',
  'in',
  'no',
  'manner',
  'account'],
 ['it',
  'was',
  'of',
  'very',
  'brief',
  'duration',
  'but',
  'while',
  'it',
  'lasted',
  'resembled',
  'nothing',
  'in',
  'the',
  'world',
  'of',
  'which',
  'i',
  'had',
  'any',
  'previous',
  'experience'],
 ['it',
  'is',
  'needless',
  'to',
  'say',
  'that',
  'i',
  'became',
  'excessively',
  'alarmed',
  'having',
  'in',
  'the',
  'first',
  'instance',
  'attributed',
  'the',
  'noise',
  'to',
  'the',
  'bursting',
  'of',
  'the',
  'balloon'],
 ['i',
  'examined',
  'all',
  'my',
  'apparatus',
  'however',
  'with',
  'great',
  'attention',
  'and',
  'could',
  'discover',
  'nothing',
  'out',
  'of',
  'order'],
 ['a',
  'great',
  'part',
  'of',
  'the',
  'day',
  'in',
  'meditating',
  'upon',
  'an',
  'occurrence',
  'so',
  'extraordinary',
  'but',
  'could',
  'find',
  'no',
  'means',
  'whatever',
  'of',
  'accounting',
  'for',
  'it'],
 ['went',
  'to',
  'bed',
  'dissatisfied',
  'and',
  'in',
  'a',
  'state',
  'of',
  'great',
  'anxiety',
  'and',
  'agitation'],
 ['11th'],
 ['found',
  'a',
  'startling',
  'diminution',
  'in',
  'the',
  'apparent',
  'diameter',
  'of',
  'the',
  'earth',
  'and',
  'a',
  'considerable',
  'increase',
  'now',
  'observable',
  'for',
  'the',
  'first',
  'time',
  'in',
  'that',
  'of',
  'the',
  'moon',
  'itself',
  'which',
  'wanted',
  'only',
  'a',
  'few',
  'days',
  'of',
  'being',
  'full'],
 ['it',
  'now',
  'required',
  'long',
  'and',
  'excessive',
  'labor',
  'to',
  'condense',
  'within',
  'the',
  'chamber',
  'sufficient',
  'atmospheric',
  'air',
  'for',
  'the',
  'sustenance',
  'of',
  'life'],
 ['12th'],
 ['a',
  'singular',
  'alteration',
  'took',
  'place',
  'in',
  'regard',
  'to',
  'the',
  'direction',
  'of',
  'the',
  'balloon',
  'and',
  'although',
  'fully',
  'anticipated',
  'afforded',
  'me',
  'the',
  'most',
  'unequivocal',
  'delight'],
 ['having',
  'reached',
  'in',
  'its',
  'former',
  'course',
  'about',
  'the',
  'twentieth',
  'parallel',
  'of',
  'southern',
  'latitude',
  'it',
  'turned',
  'off',
  'suddenly',
  'at',
  'an',
  'acute',
  'angle',
  'to',
  'the',
  'eastward',
  'and',
  'thus',
  'proceeded',
  'throughout',
  'the',
  'day',
  'keeping',
  'nearly',
  'if',
  'not',
  'altogether',
  'in',
  'the',
  'exact',
  'plane',
  'of',
  'the',
  'lunar',
  'elipse'],
 ['what',
  'was',
  'worthy',
  'of',
  'remark',
  'a',
  'very',
  'perceptible',
  'vacillation',
  'in',
  'the',
  'car',
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  'a',
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  'of',
  'this',
  'change',
  'of',
  'route',
  'a',
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  'which',
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  'in',
  'a',
  'more',
  'or',
  'less',
  'degree',
  'for',
  'a',
  'period',
  'of',
  'many',
  'hours'],
 ['13th'],
 ['again',
  'very',
  'much',
  'alarmed',
  'by',
  'a',
  'repetition',
  'of',
  'the',
  'loud',
  'crackling',
  'noise',
  'which',
  'terrified',
  'me',
  'on',
  'the',
  'tenth'],
 ['thought',
  'long',
  'upon',
  'the',
  'subject',
  'but',
  'was',
  'unable',
  'to',
  'form',
  'any',
  'satisfactory',
  'conclusion'],
 ['great',
  'decrease',
  'in',
  'the',
  'earth',
  's',
  'apparent',
  'diameter',
  'which',
  'now',
  'subtended',
  'from',
  'the',
  'balloon',
  'an',
  'angle',
  'of',
  'very',
  'little',
  'more',
  'than',
  'twentyfive',
  'degrees'],
 ['the',
  'moon',
  'could',
  'not',
  'be',
  'seen',
  'at',
  'all',
  'being',
  'nearly',
  'in',
  'my',
  'zenith'],
 ['i',
  'still',
  'continued',
  'in',
  'the',
  'plane',
  'of',
  'the',
  'elipse',
  'but',
  'made',
  'little',
  'progress',
  'to',
  'the',
  'eastward'],
 ['14th'],
 ['extremely',
  'rapid',
  'decrease',
  'in',
  'the',
  'diameter',
  'of',
  'the',
  'earth'],
 ['today',
  'i',
  'became',
  'strongly',
  'impressed',
  'with',
  'the',
  'idea',
  'that',
  'the',
  'balloon',
  'was',
  'now',
  'actually',
  'running',
  'up',
  'the',
  'line',
  'of',
  'apsides',
  'to',
  'the',
  'point',
  'of',
  'perigee',
  'in',
  'other',
  'words',
  'holding',
  'the',
  'direct',
  'course',
  'which',
  'would',
  'bring',
  'it',
  'immediately',
  'to',
  'the',
  'moon',
  'in',
  'that',
  'part',
  'of',
  'its',
  'orbit',
  'the',
  'nearest',
  'to',
  'the',
  'earth'],
 ['the',
  'moon',
  'itself',
  'was',
  'directly',
  'overhead',
  'and',
  'consequently',
  'hidden',
  'from',
  'my',
  'view'],
 ['and',
  'longcontinued',
  'labor',
  'necessary',
  'for',
  'the',
  'condensation',
  'of',
  'the',
  'atmosphere'],
 ['15th'],
 ['not',
  'even',
  'the',
  'outlines',
  'of',
  'continents',
  'and',
  'seas',
  'could',
  'now',
  'be',
  'traced',
  'upon',
  'the',
  'earth',
  'with',
  'anything',
  'approaching',
  'distinctness'],
 ['about',
  'twelve',
  'o',
  'clock',
  'i',
  'became',
  'aware',
  'for',
  'the',
  'third',
  'time',
  'of',
  'that',
  'appalling',
  'sound',
  'which',
  'had',
  'so',
  'astonished',
  'me',
  'before'],
 ['it',
  'now',
  'however',
  'continued',
  'for',
  'some',
  'moments',
  'and',
  'gathered',
  'intensity',
  'as',
  'it',
  'continued'],
 ['at',
  'length',
  'while',
  'stupefied',
  'and',
  'terrorstricken',
  'i',
  'stood',
  'in',
  'expectation',
  'of',
  'i',
  'knew',
  'not',
  'what',
  'hideous',
  'destruction',
  'the',
  'car',
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  'with',
  'excessive',
  'violence',
  'and',
  'a',
  'gigantic',
  'and',
  'flaming',
  'mass',
  'of',
  'some',
  'material',
  'which',
  'i',
  'could',
  'not',
  'distinguish',
  'came',
  'with',
  'a',
  'voice',
  'of',
  'a',
  'thousand',
  'thunders',
  'roaring',
  'and',
  'booming',
  'by',
  'the',
  'balloon'],
 ['when',
  'my',
  'fears',
  'and',
  'astonishment',
  'had',
  'in',
  'some',
  'degree',
  'subsided',
  'i',
  'had',
  'little',
  'difficulty',
  'in',
  'supposing',
  'it',
  'to',
  'be',
  'some',
  'mighty',
  'volcanic',
  'fragment',
  'ejected',
  'from',
  'that',
  'world',
  'to',
  'which',
  'i',
  'was',
  'so',
  'rapidly',
  'approaching',
  'and',
  'in',
  'all',
  'probability',
  'one',
  'of',
  'that',
  'singular',
  'class',
  'of',
  'substances',
  'occasionally',
  'picked',
  'up',
  'on',
  'the',
  'earth',
  'and',
  'termed',
  'meteoric',
  'stones',
  'for',
  'want',
  'of',
  'a',
  'better',
  'appellation'],
 ['16th'],
 ['today',
  'looking',
  'upward',
  'as',
  'well',
  'as',
  'i',
  'could',
  'through',
  'each',
  'of',
  'the',
  'side',
  'windows',
  'alternately',
  'i',
  'beheld',
  'to',
  'my',
  'great',
  'delight',
  'a',
  'very',
  'small',
  'portion',
  'of',
  'the',
  'moon',
  's',
  'disk',
  'protruding',
  'as',
  'it',
  'were',
  'on',
  'all',
  'sides',
  'beyond',
  'the',
  'huge',
  'circumference',
  'of',
  'the',
  'balloon'],
 ['my',
  'agitation',
  'was',
  'extreme',
  'for',
  'i',
  'had',
  'now',
  'little',
  'doubt',
  'of',
  'soon',
  'reaching',
  'the',
  'end',
  'of',
  'my',
  'perilous',
  'voyage'],
 ['indeed',
  'the',
  'labor',
  'now',
  'required',
  'by',
  'the',
  'condenser',
  'had',
  'increased',
  'to',
  'a',
  'most',
  'oppressive',
  'degree',
  'and',
  'allowed',
  'me',
  'scarcely',
  'any',
  'respite',
  'from',
  'exertion'],
 ['was', 'a', 'matter', 'nearly', 'out', 'of', 'the', 'question'],
 ['i',
  'became',
  'quite',
  'ill',
  'and',
  'my',
  'frame',
  'trembled',
  'with',
  'exhaustion'],
 ['it',
  'was',
  'impossible',
  'that',
  'human',
  'nature',
  'could',
  'endure',
  'this',
  'state',
  'of',
  'intense',
  'suffering',
  'much',
  'longer'],
 ['during',
  'the',
  'now',
  'brief',
  'interval',
  'of',
  'darkness',
  'a',
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  'stone',
  'again',
  'passed',
  'in',
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  'vicinity',
  'and',
  'the',
  'frequency',
  'of',
  'these',
  'phenomena',
  'began',
  'to',
  'occasion',
  'me',
  'much',
  'apprehension'],
 ['17th'],
 ['this', 'morning', 'proved', 'an', 'epoch', 'in', 'my', 'voyage'],
 ['it',
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  'that',
  'on',
  'the',
  'thirteenth',
  'the',
  'earth',
  'subtended',
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  'breadth',
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  'twentyfive',
  'degrees'],
 ['on',
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  'fourteenth',
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  'fifteenth',
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  'on',
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  'the',
  'night',
  'of',
  'the',
  'sixteenth',
  'i',
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  'more',
  'than',
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  'seven',
  'degrees',
  'and',
  'fifteen',
  'minutes'],
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  'degrees',
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 ['i', 'was', 'thunderstruck'],
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  'overwhelmed'],
 ['my',
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  'me',
  'my',
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  'my',
  'hair',
  'started',
  'up',
  'on',
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 ['the',
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  'actually',
  'burst',
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  'the',
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  'with',
  'huge',
  'crags',
  'that',
  'appeared',
  'to',
  'lie',
  'loosely',
  'upon',
  'the',
  'soil',
  'and',
  'in',
  'many',
  'cases',
  'were',
  'prevented',
  'from',
  'precipitating',
  'themselves',
  'into',
  'the',
  'valleys',
  'below',
  'merely',
  'by',
  'the',
  'support',
  'of',
  'the',
  'trees',
  'against',
  'which',
  'they',
  'reclined'],
 ['deep',
  'ravines',
  'in',
  'various',
  'directions',
  'gave',
  'an',
  'air',
  'of',
  'still',
  'sterner',
  'solemnity',
  'to',
  'the',
  'scene'],
 ['the',
  'natural',
  'platform',
  'to',
  'which',
  'we',
  'had',
  'clambered',
  'was',
  'thickly',
  'overgrown',
  'with',
  'brambles',
  'through',
  'which',
  'we',
  'soon',
  'discovered',
  'that',
  'it',
  'would',
  'have',
  'been',
  'impossible',
  'to',
  'force',
  'our',
  'way',
  'but',
  'for',
  'the',
  'scythe',
  'and',
  'by',
  'direction',
  'of',
  'his',
  'master',
  'proceeded',
  'to',
  'clear',
  'for',
  'us',
  'a',
  'path',
  'to',
  'the',
  'foot',
  'of',
  'an',
  'enormously',
  'tall',
  'tuliptree',
  'which',
  'stood',
  'with',
  'some',
  'eight',
  'or',
  'ten',
  'oaks',
  'upon',
  'the',
  'level',
  'and',
  'far',
  'surpassed',
  'them',
  'all',
  'and',
  'all',
  'other',
  'trees',
  'which',
  'i',
  'had',
  'then',
  'ever',
  'seen',
  'in',
  'the',
  'beauty',
  'of',
  'its',
  'foliage',
  'and',
  'form',
  'in',
  'the',
  'wide',
  'spread',
  'of',
  'its',
  'branches',
  'and',
  'in',
  'the',
  'general',
  'majesty',
  'of',
  'its',
  'appearance'],
 ['when',
  'we',
  'reached',
  'this',
  'tree',
  'turned',
  'to',
  'and',
  'asked',
  'him',
  'if',
  'he',
  'thought',
  'he',
  'could',
  'climb',
  'it'],
 ['the',
  'old',
  'man',
  'seemed',
  'a',
  'little',
  'staggered',
  'by',
  'the',
  'question',
  'and',
  'for',
  'some',
  'moments',
  'made',
  'no',
  'reply'],
 ['at',
  'length',
  'he',
  'approached',
  'the',
  'huge',
  'trunk',
  'walked',
  'slowly',
  'around',
  'it',
  'and',
  'examined',
  'it',
  'with',
  'minute',
  'attention'],
 ['when', 'he', 'had', 'completed', 'his', 'scrutiny', 'he', 'merely', 'said'],
 ['massa', 'climb', 'any', 'tree', 'he', 'ebber', 'see', 'in', 'he', 'life'],
 ['then',
  'up',
  'with',
  'you',
  'as',
  'soon',
  'as',
  'possible',
  'for',
  'it',
  'will',
  'soon',
  'be',
  'too',
  'dark',
  'to',
  'see',
  'what',
  'we',
  'are',
  'about'],
 ['how', 'far', 'mus', 'go', 'up', 'massa', 'inquired'],
 ['up',
  'the',
  'main',
  'trunk',
  'first',
  'and',
  'then',
  'i',
  'will',
  'tell',
  'you',
  'which',
  'way',
  'to',
  'go',
  'and',
  'here',
  'stop'],
 ['take', 'this', 'beetle', 'with', 'you'],
 ['bug',
  'de',
  'goole',
  'bug',
  'cried',
  'the',
  'negro',
  'drawing',
  'back',
  'in',
  'dismay',
  'what',
  'for',
  'mus',
  'tote',
  'de',
  'bug',
  'way',
  'up',
  'de',
  'tree',
  'dn',
  'if',
  'i',
  'do'],
 ['if',
  'you',
  'are',
  'afraid',
  'a',
  'great',
  'big',
  'negro',
  'like',
  'you',
  'to',
  'take',
  'hold',
  'of',
  'a',
  'harmless',
  'little',
  'dead',
  'beetle',
  'why',
  'you',
  'can',
  'carry',
  'it',
  'up',
  'by',
  'this',
  'string',
  'but',
  'if',
  'you',
  'do',
  'not',
  'take',
  'it',
  'up',
  'with',
  'you',
  'in',
  'some',
  'way',
  'i',
  'shall',
  'be',
  'under',
  'the',
  'necessity',
  'of',
  'breaking',
  'your',
  'head',
  'with',
  'this',
  'shovel'],
 ['what',
  'de',
  'matter',
  'now',
  'massa',
  'said',
  'evidently',
  'shamed',
  'into',
  'compliance',
  'always',
  'want',
  'for',
  'to',
  'raise',
  'fuss',
  'wid',
  'old',
  'nigger'],
 ['only', 'funnin', 'any', 'how'],
 ['feered', 'de', 'bug'],
 ['what',
  'i',
  'keer',
  'for',
  'de',
  'bug',
  'here',
  'he',
  'took',
  'cautiously',
  'hold',
  'of',
  'the',
  'extreme',
  'end',
  'of',
  'the',
  'string',
  'and',
  'maintaining',
  'the',
  'insect',
  'as',
  'far',
  'from',
  'his',
  'person',
  'as',
  'circumstances',
  'would',
  'permit',
  'prepared',
  'to',
  'ascend',
  'the',
  'tree'],
 ['in',
  'youth',
  'the',
  'tuliptree',
  'or',
  'the',
  'most',
  'magnificent',
  'of',
  'american',
  'foresters',
  'has',
  'a',
  'trunk',
  'peculiarly',
  'smooth',
  'and',
  'often',
  'rises',
  'to',
  'a',
  'great',
  'height',
  'without',
  'lateral',
  'branches',
  'but',
  'in',
  'its',
  'riper',
  'age',
  'the',
  'bark',
  'becomes',
  'gnarled',
  'and',
  'uneven',
  'while',
  'many',
  'short',
  'limbs',
  'make',
  'their',
  'appearance',
  'on',
  'the',
  'stem'],
 ['thus',
  'the',
  'difficulty',
  'of',
  'ascension',
  'in',
  'the',
  'present',
  'case',
  'lay',
  'more',
  'in',
  'semblance',
  'than',
  'in',
  'reality'],
 ['embracing',
  'the',
  'huge',
  'cylinder',
  'as',
  'closely',
  'as',
  'possible',
  'with',
  'his',
  'arms',
  'and',
  'knees',
  'seizing',
  'with',
  'his',
  'hands',
  'some',
  'projections',
  'and',
  'resting',
  'his',
  'naked',
  'toes',
  'upon',
  'others',
  'after',
  'one',
  'or',
  'two',
  'narrow',
  'escapes',
  'from',
  'falling',
  'at',
  'length',
  'wriggled',
  'himself',
  'into',
  'the',
  'first',
  'great',
  'fork',
  'and',
  'seemed',
  'to',
  'consider',
  'the',
  'whole',
  'business',
  'as',
  'virtually',
  'accomplished'],
 ['the',
  'risk',
  'of',
  'the',
  'achievement',
  'was',
  'in',
  'fact',
  'now',
  'over',
  'although',
  'the',
  'climber',
  'was',
  'some',
  'sixty',
  'or',
  'seventy',
  'feet',
  'from',
  'the',
  'ground'],
 ['way', 'mus', 'go', 'now', 'he', 'asked'],
 ['up',
  'the',
  'largest',
  'branch',
  'the',
  'one',
  'on',
  'this',
  'side',
  'said'],
 ['the',
  'negro',
  'obeyed',
  'him',
  'promptly',
  'and',
  'apparently',
  'with',
  'but',
  'little',
  'trouble',
  'ascending',
  'higher',
  'and',
  'higher',
  'until',
  'no',
  'glimpse',
  'of',
  'his',
  'squat',
  'figure',
  'could',
  'be',
  'obtained',
  'through',
  'the',
  'dense',
  'foliage',
  'which',
  'enveloped',
  'it'],
 ['presently',
  'his',
  'voice',
  'was',
  'heard',
  'in',
  'a',
  'sort',
  'of',
  'halloo'],
 ['how', 'much', 'fudder', 'is', 'got', 'for', 'go'],
 ['how', 'high', 'up', 'are', 'you', 'asked'],
 ['so',
  'fur',
  'replied',
  'the',
  'negro',
  'can',
  'see',
  'de',
  'sky',
  'fru',
  'de',
  'top',
  'ob',
  'de',
  'tree'],
 ['mind', 'the', 'sky', 'but', 'attend', 'to', 'what', 'i', 'say'],
 ['look',
  'down',
  'the',
  'trunk',
  'and',
  'count',
  'the',
  'limbs',
  'below',
  'you',
  'on',
  'this',
  'side'],
 ['how', 'many', 'limbs', 'have', 'you', 'passed'],
 ['one',
  'two',
  'tree',
  'four',
  'fibe',
  'i',
  'done',
  'pass',
  'fibe',
  'big',
  'limb',
  'massa',
  'pon',
  'dis',
  'side'],
 ['then', 'go', 'one', 'limb', 'higher'],
 ['in',
  'a',
  'few',
  'minutes',
  'the',
  'voice',
  'was',
  'heard',
  'again',
  'announcing',
  'that',
  'the',
  'seventh',
  'limb',
  'was',
  'attained'],
 ['now',
  'cried',
  'evidently',
  'much',
  'excited',
  'i',
  'want',
  'you',
  'to',
  'work',
  'your',
  'way',
  'out',
  'upon',
  'that',
  'limb',
  'as',
  'far',
  'as',
  'you',
  'can'],
 ['if',
  'you',
  'see',
  'anything',
  'strange',
  'let',
  'me',
  'know',
  'by',
  'this',
  'time',
  'what',
  'little',
  'doubt',
  'i',
  'might',
  'have',
  'entertained',
  'of',
  'my',
  'poor',
  'friend',
  's',
  'insanity',
  'was',
  'put',
  'finally',
  'at',
  'rest'],
 ['i',
  'had',
  'no',
  'alternative',
  'but',
  'to',
  'conclude',
  'him',
  'stricken',
  'with',
  'lunacy',
  'and',
  'i',
  'became',
  'seriously',
  'anxious',
  'about',
  'getting',
  'him',
  'home'],
 ['while',
  'i',
  'was',
  'pondering',
  'upon',
  'what',
  'was',
  'best',
  'to',
  'be',
  'done',
  's',
  'voice',
  'was',
  'again',
  'heard'],
 ['feerd',
  'for',
  'to',
  'ventur',
  'pon',
  'dis',
  'limb',
  'berry',
  'far',
  'tis',
  'dead',
  'limb',
  'putty',
  'much',
  'all',
  'de',
  'way'],
 ['you',
  'say',
  'it',
  'was',
  'a',
  'dead',
  'limb',
  'cried',
  'in',
  'a',
  'quavering',
  'voice'],
 ['massa',
  'him',
  'dead',
  'as',
  'de',
  'doornail',
  'done',
  'up',
  'for',
  'sartain',
  'done',
  'departed',
  'dis',
  'here',
  'life'],
 ['what',
  'in',
  'the',
  'name',
  'heaven',
  'shall',
  'i',
  'do',
  'asked',
  'seemingly',
  'in',
  'the',
  'greatest',
  'distress'],
 ['do',
  'said',
  'i',
  'glad',
  'of',
  'an',
  'opportunity',
  'to',
  'interpose',
  'a',
  'word',
  'why',
  'come',
  'home',
  'and',
  'go',
  'to',
  'bed'],
 ['now', 'that', 's', 'a', 'fine', 'fellow'],
 ['it',
  's',
  'getting',
  'late',
  'and',
  'besides',
  'you',
  'remember',
  'your',
  'promise'],
 ['cried',
  'he',
  'without',
  'heeding',
  'me',
  'in',
  'the',
  'least',
  'do',
  'you',
  'hear',
  'me'],
 ['hear', 'you', 'ebber', 'so', 'plain'],
 ['the',
  'wood',
  'well',
  'then',
  'with',
  'your',
  'knife',
  'and',
  'see',
  'if',
  'you',
  'think',
  'it',
  'very',
  'rotten'],
 ['rotten',
  'massa',
  'sure',
  'nuff',
  'replied',
  'the',
  'negro',
  'in',
  'a',
  'few',
  'moments',
  'but',
  'not',
  'so',
  'berry',
  'rotten',
  'as',
  'mought',
  'be'],
 ['ventur',
  'out',
  'leetle',
  'way',
  'pon',
  'de',
  'limb',
  'by',
  'myself',
  'dat',
  's',
  'true'],
 ['by', 'yourself', 'what', 'do', 'you', 'mean'],
 ['why', 'i', 'mean', 'de', 'bug'],
 ['berry', 'hebby', 'bug'],
 ['i',
  'drop',
  'him',
  'down',
  'fuss',
  'and',
  'den',
  'de',
  'limb',
  'won',
  't',
  'break',
  'wid',
  'just',
  'de',
  'weight',
  'ob',
  'one',
  'nigger'],
 ['you',
  'infernal',
  'scoundrel',
  'cried',
  'apparently',
  'much',
  'relieved',
  'what',
  'do',
  'you',
  'mean',
  'by',
  'telling',
  'me',
  'such',
  'nonsense',
  'as',
  'that'],
 ['as',
  'sure',
  'as',
  'you',
  'drop',
  'that',
  'beetle',
  'i',
  'll',
  'break',
  'your',
  'neck'],
 ['look', 'here', 'do', 'you', 'hear', 'me'],
 ['massa', 'needn', 't', 'hollo', 'at', 'poor', 'nigger', 'dat', 'style'],
 ['well'],
 ['now',
  'listen',
  'if',
  'you',
  'will',
  'venture',
  'out',
  'on',
  'the',
  'limb',
  'as',
  'far',
  'as',
  'you',
  'think',
  'safe',
  'and',
  'not',
  'let',
  'go',
  'the',
  'beetle',
  'i',
  'll',
  'make',
  'you',
  'a',
  'present',
  'of',
  'a',
  'silver',
  'dollar',
  'as',
  'soon',
  'as',
  'you',
  'get',
  'down'],
 ['i',
  'm',
  'gwine',
  'deed',
  'i',
  'is',
  'replied',
  'the',
  'negro',
  'very',
  'promptly',
  'mos',
  'out',
  'to',
  'the',
  'eend',
  'now'],
 ['to',
  'the',
  'end',
  'here',
  'fairly',
  'screamed',
  'do',
  'you',
  'say',
  'you',
  'are',
  'out',
  'to',
  'the',
  'end',
  'of',
  'that',
  'limb'],
 ['soon', 'be', 'to', 'de', 'eend', 'massa', 'oooooh'],
 ['lorgolamarcy'],
 ['what', 'is', 'dis', 'here', 'pon', 'de', 'tree'],
 ['well', 'cried', 'highly', 'delighted', 'what', 'is', 'it'],
 ['why',
  'taint',
  'noffin',
  'but',
  'a',
  'skull',
  'somebody',
  'bin',
  'lef',
  'him',
  'head',
  'up',
  'de',
  'tree',
  'and',
  'de',
  'crows',
  'done',
  'gobble',
  'ebery',
  'bit',
  'ob',
  'de',
  'meat',
  'off'],
 ...]

Generate word embeddings with Gensim's library

model = word2vec.Word2Vec(corpus, size=246, window=window, min_count=200, workers=4)

Visualize with tSNE

Generate coordinates to plot

coords = pd.DataFrame(index=range(len(model.wv.vocab)))
coords['label'] = [w for w in model.wv.vocab]
coords['vector'] = coords['label'].apply(lambda x: model.wv.get_vector(x))

coords.head()

	label	vector
0	the	[0.22036165, 0.26431423, -0.2840596, 0.0789844...
1	late	[-0.091123305, -0.119603, -0.14193687, -0.0994...
2	from	[-0.38256493, -0.25111318, 0.5238992, 0.039146...
3	that	[-0.15131615, 0.36850616, -0.21218912, 0.04042...
4	to	[-0.3417644, 0.112480424, 0.8044965, -0.105765...

Use ScikitLearn's TSNE library

tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23)
tsne_values = tsne_model.fit_transform(coords['vector'].tolist())

coords['x'] = tsne_values[:,0]
coords['y'] = tsne_values[:,1]

coords.head()

	label	vector	x	y
0	the	[0.22036165, 0.26431423, -0.2840596, 0.0789844...	9.654496	3.837436
1	late	[-0.091123305, -0.119603, -0.14193687, -0.0994...	-0.693029	5.529316
2	from	[-0.38256493, -0.25111318, 0.5238992, 0.039146...	12.801660	2.025463
3	that	[-0.15131615, 0.36850616, -0.21218912, 0.04042...	-3.497712	2.166096
4	to	[-0.3417644, 0.112480424, 0.8044965, -0.105765...	-7.868403	-0.119167

Plot the coordinates

px.scatter(coords, 'x', 'y', text='label', height=1000).update_traces(mode='text')

Semantic Algebra

Analogy

$A : B :: C : D? \rightarrow B - A + C = D$

def complete_analogy(A, B, C, n=2):
    try:
        return model.wv.most_similar(positive=[B, C], negative=[A])[0:n]
    except KeyError as e:
        print('Error:', e)
        return None

complete_analogy('i', 'we', 'you')

[('us', 0.7015376687049866), ('our', 0.558246910572052)]

complete_analogy('man', 'woman', 'he')

[('she', 0.7519389390945435), ('feeling', 0.4981950521469116)]

complete_analogy('man','boy','woman')

[('girl', 0.8022274374961853), ('child', 0.7688707113265991)]

Sentiment Analysis

# set up 
#data_dir = 'data/'

novels_csv = 'TOKEN2.csv'
vocab_csv = 'VOCAB2.csv'
lib_csv = 'LIB3.csv'
bow_csv = 'BOW.csv'

# For TOKENS
OHCO = ['book_id', 'chap_num', 'para_num', 'sent_num', 'token_num']
BOOKS = OHCO[:1]
CHAPS = OHCO[:2]
PARAS = OHCO[:3]
SENTS = OHCO[:4]

salex_csv = 'salex_nrc.csv'
emo_cols = "anger anticipation disgust fear joy sadness surprise trust polarity".split()

import pandas as pd
import numpy as np
import seaborn as sns
import plotly_express as px
from IPython.core.display import display, HTML

sns.set()
%matplotlib inline

Acquire Data

TOKENS = pd.read_csv(novels_csv).set_index(OHCO)
VOCAB = pd.read_csv(vocab_csv).set_index('term_id')
LIB = pd.read_csv(lib_csv).set_index('book_id')
BOW = pd.read_csv(bow_csv)

# Add token_str to BOW
BOW['term_str'] = BOW.term_id.map(VOCAB.term_str)
BOW = BOW.set_index(['book_id','chap_num','term_id'])

BOW.sort_index()

			n	c	term_str
book_id	chap_num	term_id
1	1	5	1	1	005484
		10	3	1	1
		13	1	1	1000
		16	1	1	10600
		17	1	1	1080
...	...	...	...	...	...
3795	24	29449	2	1	yellow
		29473	104	1	you
		29474	4	1	young
		29475	2	1	younger
		29478	15	1	your

304741 rows × 3 columns

LIB

	book_id.1	book_title	book_file	author	title
book_id
2787	An Old - fashioned Girl, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_AN_OLD-FASHIONED_GIRL...	2787	Alcott	An Old - fashioned Girl
2726	Eight Cousins, by Louisa M. Alcott	epubs_\ALCOTT_MAY_LOUISA_EIGHT_COUSINS-pg2726.txt	2726	Alcott	Eight Cousins
2786	Jack and Jill, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JACK_AND_JILL-pg2786.txt	2786	Alcott	Jack and Jill
3499	Jo's Boys, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_JO’S_BOYS-pg3499.txt	3499	Alcott	Jo's Boys
2788	Little Men, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_MEN-pg2788.txt	2788	Alcott	Little Men
514	Little Women, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_LITTLE_WOMEN-pg514.txt	514	Alcott	Little Women
2804	Rose in Bloom, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_ROSE_IN_BLOOM-pg2804.txt	2804	Alcott	Rose in Bloom
3795	Under the Lilacs, by Louisa May Alcott	epubs_\ALCOTT_MAY_LOUISA_UNDER_THE_LILACS-pg37...	3795	Alcott	Under the Lilacs
1	The Works of Edgar Allan Poe Volume 1 (of 5) o...	epubs/2147-0.txt	1	Poe	The Works of Edgar Allan Poe Volume 1 (of 5) o...
2	The Works of Edgar Allan Poe Volume 2 (of 5) o...	epubs/2148-0.txt	2	Poe	The Works of Edgar Allan Poe Volume 2 (of 5) o...
3	The Works of Edgar Allan Poe Volume 3 (of 5) o...	epubs/2149-0.txt	3	Poe	The Works of Edgar Allan Poe Volume 3 (of 5) o...
4	The Works of Edgar Allan Poe Volume 4 (of 5) o...	epubs/2150-0.txt	4	Poe	The Works of Edgar Allan Poe Volume 4 (of 5) o...
5	The Works of Edgar Allan Poe Volume 5 (of 5) o...	epubs/2151-0.txt	5	Poe	The Works of Edgar Allan Poe Volume 5 (of 5) o...

# book name and id 
old = 2787
eight = 2726
jackjill = 2786
jo = 3499
littleM = 2788
littleW = 514
rose = 2804
under = 3795
poe1 = 1
poe2 = 2
poe3 = 3
poe4 = 4
poe5 = 5

Get SA Lexicon

salex_csv = 'salex_nrc.csv'

SALEX = pd.read_csv(salex_csv).set_index('term_str')
SALEX.columns = [col.replace('nrc_','') for col in SALEX.columns]
SALEX['polarity'] = SALEX.positive - SALEX.negative

SALEX

	anger	anticipation	disgust	fear	joy	negative	positive	sadness	surprise	trust	polarity
term_str
abandon	0	0	0	1	0	1	0	1	0	0	-1
abandoned	1	0	0	1	0	1	0	1	0	0	-1
abandonment	1	0	0	1	0	1	0	1	1	0	-1
abduction	0	0	0	1	0	1	0	1	1	0	-1
aberration	0	0	1	0	0	1	0	0	0	0	-1
...	...	...	...	...	...	...	...	...	...	...	...
young	0	1	0	0	1	0	1	0	1	0	1
youth	1	1	0	1	1	0	1	0	1	0	1
zeal	0	1	0	0	1	0	1	0	1	1	1
zealous	0	0	0	0	1	0	1	0	0	1	1
zest	0	1	0	0	1	0	1	0	0	1	1

3688 rows × 11 columns

Compute Sentiment

Combine SALEX with VOCAB

VOCAB.head()

	term_rank	term_str	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	zipf_k2	zipf_k3	p2	h	df	idf	tfidf_sum	h2	x_factor2
term_id
26151	1	the	58042	0	1	the	DT	1	1.963798	58042	58042	1.963798	0.052274	0.222570	247	0.001755	0.022318	7.652003	0.000000
1110	2	and	41247	0	1	and	CC	2	1.395554	82494	82494	2.791108	0.037148	0.176474	247	0.001755	0.016572	7.820398	5.420687
17817	3	of	29442	0	1	of	IN	3	0.996143	88326	88326	2.988429	0.026516	0.138865	247	0.001755	0.011051	7.532582	8.275387
26510	4	to	28593	0	1	to	TO	4	0.967418	114372	114372	3.869671	0.025752	0.135947	247	0.001755	0.011093	7.777130	10.781392
219	5	a	26310	0	1	a	DT	5	0.890175	131550	131550	4.450873	0.023695	0.127937	247	0.001755	0.010247	7.782000	12.524645

len(VOCAB)

29556

V = pd.concat([VOCAB.reset_index().set_index('term_str'), SALEX], join='inner', axis=1)\
    .reset_index().set_index('term_id')

V.head()

	term_str	term_rank	n	num	stop	stem_porter	pos_max	term_rank2	p	zipf_k	...	anticipation	disgust	fear	joy	negative	positive	sadness	surprise	trust	polarity
term_id
11482	good	69	2193	0	0	good	JJ	68	0.074198	151317	...	1	0	0	1	0	1	0	1	1	1
10624	found	142	975	0	0	found	VBD	139	0.032988	138450	...	0	0	0	1	0	1	0	0	1	1
16868	mother	144	963	0	0	mother	NN	141	0.032582	138672	...	1	0	0	1	1	1	1	0	1	0
29474	young	151	917	0	0	young	JJ	147	0.031026	138467	...	1	0	0	1	0	1	0	1	0	1
3230	boy	167	828	0	0	boy	NN	162	0.028015	138276	...	0	1	0	0	1	0	0	0	0	-1

5 rows × 30 columns

len(V)

2678

V.to_csv('Vocab_Sentiment.csv')

Combine VOCAB with BOW

BOWIDX = BOW.index
B = BOW.merge(V, on='term_str', how='left')
B.index = BOWIDX
B = B.dropna()

# didnt have tf, tfidf columns
bcols = ['n_x', 'term_str', 'term_rank', 'n_y', 'num', 'pos_max', 'term_rank2', 'p', 
'tfidf_sum', 'anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust', 'polarity']

B = B[bcols]

for col in emo_cols:
    B[col] = B[col] * B.tfidf_sum # used to be B.tfidf

B.head()

			n_x	term_str	term_rank	n_y	num	pos_max	term_rank2	p	tfidf_sum	anger	anticipation	disgust	fear	joy	negative	positive	sadness	surprise	trust	polarity
book_id	chap_num	term_id
1	1	224	2	abandoned	4054.0	21.0	0.0	VBN	585.0	0.000711	0.005090	0.00509	0.0	0.0	0.005090	0.000000	1.0	0.0	0.005090	0.000000	0.0	-0.005090
		295	2	absence	1545.0	65.0	0.0	NN	541.0	0.002199	0.008545	0.00000	0.0	0.0	0.008545	0.000000	1.0	0.0	0.008545	0.000000	0.0	-0.008545
		336	1	abyss	4196.0	20.0	0.0	NN	586.0	0.000677	0.006253	0.00000	0.0	0.0	0.006253	0.000000	1.0	0.0	0.006253	0.000000	0.0	-0.006253
		363	3	accident	1288.0	81.0	0.0	NN	525.0	0.002741	0.010454	0.00000	0.0	0.0	0.010454	0.000000	1.0	0.0	0.010454	0.010454	0.0	-0.010454
		382	2	accomplish	5340.0	14.0	0.0	VB	592.0	0.000474	0.002949	0.00000	0.0	0.0	0.000000	0.002949	0.0	1.0	0.000000	0.000000	0.0	0.002949

B.tfidf_sum

book_id  chap_num  term_id
1        1         224        0.005090
                   295        0.008545
                   336        0.006253
                   363        0.010454
                   382        0.002949
                                ...   
3795     24        28947      0.017297
                   29240      0.014370
                   29244      0.011817
                   29283      0.013311
                   29474      0.019783
Name: tfidf_sum, Length: 43398, dtype: float64

Explore Emotion Words and POS

B.pos_max.value_counts().sort_values().plot.barh()

<matplotlib.axes._subplots.AxesSubplot at 0x2470b936c18>

EMO_BOOKS = B.groupby(['book_id'])[emo_cols].mean()
EMO_CHAPS = B.groupby(['book_id','chap_num'])[emo_cols].mean()

EMO_BOOKS.index = LIB.book_title

EMO_BOOKS.plot.bar(figsize=(25,10))

<matplotlib.axes._subplots.AxesSubplot at 0x2470b9064e0>

Compare Texts

EIGHT = EMO_CHAPS.loc[eight].copy()
JACKJILL = EMO_CHAPS.loc[jackjill].copy()
OLD = EMO_CHAPS.loc[old].copy()
JO = EMO_CHAPS.loc[jo].copy()
LITTLEM = EMO_CHAPS.loc[littleM].copy()
LITTLEW = EMO_CHAPS.loc[littleW].copy()
ROSE = EMO_CHAPS.loc[rose].copy()
UNDER = EMO_CHAPS.loc[under].copy()
POE1 = EMO_CHAPS.loc[poe1].copy()
POE2 = EMO_CHAPS.loc[poe2].copy()
POE3 = EMO_CHAPS.loc[poe3].copy()
POE4 = EMO_CHAPS.loc[poe4].copy()
POE5 = EMO_CHAPS.loc[poe5].copy()

EIGHT.mean().sort_values().plot.barh();

JACKJILL.mean().sort_values().plot.barh();

OLD.mean().sort_values().plot.barh();

JO.mean().sort_values().plot.barh();

LITTLEM.mean().sort_values().plot.barh();

LITTLEW.mean().sort_values().plot.barh();

ROSE.mean().sort_values().plot.barh();

UNDER.mean().sort_values().plot.barh();

POE1.mean().sort_values().plot.barh();

POE2.mean().sort_values().plot.barh();

POE3.mean().sort_values().plot.barh();

POE4.mean().sort_values().plot.barh();

POE5.mean().sort_values().plot.barh();

What is Trust?

trust_words = pd.Series(SALEX[SALEX['trust'] == 1].index.tolist())

trust_words.sample(10)

657           praise
395              god
467        indelible
475         innocent
402    grandchildren
227            crisp
363        formative
154           cogent
809        statement
419             hail
dtype: object

Mean Sentiment by Chapter

EIGHT_chaps = EMO_CHAPS.loc[eight] 
JACKJILL_chaps = EMO_CHAPS.loc[jackjill] 
OLD_chaps = EMO_CHAPS.loc[old]
JO_chaps = EMO_CHAPS.loc[jo]
LITTLEM_chaps = EMO_CHAPS.loc[littleM]
LITTLEW_chaps = EMO_CHAPS.loc[littleW]
ROSE_chaps = EMO_CHAPS.loc[rose]
UNDER_chaps = EMO_CHAPS.loc[under]
POE1_chaps = EMO_CHAPS.loc[poe1]
POE2_chaps = EMO_CHAPS.loc[poe2]
POE3_chaps = EMO_CHAPS.loc[poe3]
POE4_chaps = EMO_CHAPS.loc[poe4]
POE5_chaps = EMO_CHAPS.loc[poe5]

EIGHT_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.002218	0.004540	0.001484	0.003062	0.006274	0.003834	0.002254	0.004984	0.003144
2	0.001857	0.004506	0.001574	0.002005	0.005895	0.002520	0.003465	0.004947	0.004326
3	0.001897	0.003304	0.001740	0.002407	0.004911	0.003439	0.001908	0.004843	0.002505
4	0.001169	0.004936	0.001146	0.002664	0.005836	0.003226	0.001989	0.005441	0.003755
5	0.001603	0.004802	0.001289	0.001786	0.006715	0.002772	0.003053	0.006427	0.004784
6	0.001410	0.003965	0.001530	0.002149	0.006129	0.002271	0.002076	0.005548	0.004771
7	0.001307	0.004421	0.001533	0.001956	0.006353	0.002553	0.002243	0.005287	0.005002
8	0.001903	0.004445	0.001755	0.002628	0.005297	0.003283	0.002914	0.005953	0.003931
9	0.001636	0.004147	0.001135	0.002048	0.005737	0.002030	0.002395	0.004421	0.004652
10	0.001551	0.004189	0.000969	0.001662	0.006952	0.002336	0.002322	0.005112	0.005601
11	0.001914	0.004234	0.002004	0.002499	0.004771	0.004071	0.002088	0.005307	0.001408
12	0.001807	0.003917	0.001398	0.001972	0.005106	0.002532	0.001875	0.005184	0.003615
13	0.001452	0.004937	0.001366	0.001420	0.006662	0.002539	0.002015	0.006146	0.006473
14	0.002259	0.003739	0.001730	0.002807	0.004309	0.003009	0.002451	0.004494	0.001795
15	0.002683	0.003410	0.001973	0.002740	0.004223	0.003273	0.002186	0.004021	0.001150
16	0.001189	0.004906	0.000975	0.001206	0.007030	0.001765	0.002434	0.006736	0.007068
17	0.001719	0.004224	0.001475	0.002646	0.005212	0.002327	0.002173	0.006005	0.003629
18	0.001942	0.003076	0.001596	0.002206	0.003953	0.002826	0.001835	0.004597	0.001694
19	0.001603	0.003856	0.001577	0.003045	0.005400	0.002814	0.002238	0.005472	0.003388
20	0.001401	0.003840	0.001335	0.001709	0.006375	0.002328	0.002444	0.005304	0.004857
21	0.002329	0.003440	0.002271	0.003388	0.003266	0.004366	0.001822	0.004168	-0.001413
22	0.001253	0.004491	0.001549	0.001913	0.007078	0.002907	0.002740	0.006393	0.006508
23	0.002047	0.002951	0.001770	0.002678	0.004523	0.002898	0.001582	0.005336	0.002091
24	0.001813	0.003609	0.001233	0.002116	0.005086	0.002331	0.001866	0.005287	0.004287

JACKJILL_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.001793	0.003098	0.001548	0.003142	0.004031	0.003306	0.001774	0.004641	0.001883
2	0.002136	0.004500	0.002011	0.003764	0.005664	0.004873	0.002492	0.005253	0.002350
3	0.001995	0.003372	0.001716	0.002380	0.004607	0.003098	0.002114	0.003975	0.001979
4	0.001551	0.005056	0.001528	0.002388	0.006800	0.003040	0.002172	0.004980	0.004206
5	0.001435	0.004892	0.001685	0.001621	0.006808	0.001647	0.002754	0.006024	0.005665
6	0.001556	0.004274	0.001887	0.001990	0.005831	0.003013	0.002687	0.004382	0.003402
7	0.002273	0.003367	0.001721	0.002673	0.004079	0.003289	0.001747	0.004715	0.001162
8	0.001661	0.003811	0.001556	0.002072	0.004923	0.002559	0.001561	0.003987	0.002300
9	0.001677	0.003164	0.001471	0.002239	0.004627	0.002819	0.002045	0.005261	0.003203
10	0.001525	0.004198	0.001256	0.001685	0.006452	0.002793	0.002565	0.004967	0.004692
11	0.002098	0.003459	0.001673	0.002635	0.003695	0.002918	0.002174	0.003999	0.000797
12	0.002082	0.003538	0.001507	0.002362	0.004691	0.002830	0.002100	0.004156	0.002307
13	0.002334	0.003711	0.001557	0.002699	0.004134	0.003165	0.001733	0.004806	0.001524
14	0.002097	0.003741	0.001220	0.002246	0.005029	0.002365	0.002561	0.005777	0.003459
15	0.001323	0.004431	0.001196	0.001718	0.006693	0.002216	0.002626	0.006279	0.005608
16	0.001719	0.004491	0.001558	0.001606	0.006967	0.002824	0.002781	0.004992	0.004757
17	0.001504	0.004111	0.001619	0.002027	0.006080	0.002823	0.002373	0.005227	0.003725
18	0.001134	0.004201	0.001405	0.001873	0.006954	0.002709	0.002488	0.005224	0.004786
19	0.001900	0.003211	0.001285	0.002258	0.004958	0.002540	0.002023	0.004383	0.002789
20	0.001572	0.004797	0.001190	0.002679	0.006094	0.003375	0.002296	0.006167	0.004340
21	0.001636	0.003868	0.001416	0.001877	0.005589	0.002830	0.002096	0.004434	0.003245
22	0.001795	0.004106	0.001806	0.002313	0.005973	0.003341	0.002766	0.005176	0.004031
23	0.001724	0.003598	0.001667	0.001674	0.005229	0.002576	0.002374	0.004628	0.003664
24	0.001255	0.003908	0.001044	0.001269	0.005394	0.002049	0.002155	0.005630	0.005376

OLD_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.002019	0.004333	0.001820	0.002760	0.005201	0.003030	0.002605	0.004602	0.002061
2	0.002308	0.003560	0.001850	0.002333	0.004168	0.003346	0.002060	0.003974	0.001395
3	0.002285	0.003932	0.001803	0.002519	0.004768	0.003258	0.001873	0.005167	0.002094
4	0.002103	0.003894	0.001705	0.002674	0.005317	0.003370	0.002277	0.004908	0.002476
5	0.002975	0.003015	0.002636	0.002908	0.003972	0.003532	0.001871	0.004262	0.000745
6	0.001952	0.003586	0.001809	0.002469	0.004458	0.002916	0.002014	0.004083	0.001611
7	0.001587	0.003562	0.001582	0.002506	0.005612	0.003146	0.002041	0.004299	0.002788
8	0.001483	0.003932	0.001619	0.002230	0.005600	0.003119	0.002221	0.005099	0.003772
9	0.001931	0.003886	0.001728	0.002572	0.005390	0.004147	0.002012	0.005199	0.002765
10	0.001782	0.003748	0.001854	0.002521	0.005168	0.002987	0.002164	0.004739	0.003031
11	0.001772	0.003217	0.001565	0.002056	0.004640	0.002767	0.001744	0.004184	0.002719
12	0.001934	0.003564	0.001490	0.002042	0.005003	0.002539	0.002082	0.004681	0.003247
13	0.001824	0.004864	0.001557	0.002002	0.006079	0.002832	0.002649	0.004470	0.003765
14	0.001775	0.004058	0.001842	0.002442	0.005360	0.003190	0.001903	0.004965	0.002750
15	0.002207	0.003271	0.001556	0.002777	0.004233	0.003577	0.001657	0.003686	0.000396
16	0.002339	0.003886	0.001616	0.002234	0.004992	0.003206	0.002122	0.004717	0.002319
17	0.002030	0.003634	0.001464	0.002572	0.004095	0.003225	0.002049	0.004629	0.002040
18	0.001869	0.003910	0.001770	0.002913	0.005627	0.002773	0.002258	0.004812	0.002795
19	0.001533	0.003708	0.001598	0.002243	0.005413	0.002807	0.002265	0.005518	0.003532

JO_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
23	0.001711	0.003629	0.001553	0.002066	0.005673	0.002821	0.002080	0.005062	0.003484
24	0.001716	0.003998	0.001454	0.002393	0.006021	0.002311	0.002060	0.005355	0.004496
25	0.001746	0.003985	0.001358	0.002538	0.004789	0.002745	0.002161	0.004625	0.002666
26	0.001357	0.003812	0.001049	0.001938	0.005070	0.002381	0.002425	0.004762	0.003803
27	0.001966	0.003334	0.001484	0.002175	0.004628	0.002569	0.001737	0.004079	0.002096
28	0.001792	0.003518	0.001669	0.002832	0.004641	0.002907	0.001924	0.005304	0.002986
29	0.002356	0.003099	0.001745	0.003464	0.003780	0.003648	0.001964	0.004219	0.000951
30	0.001594	0.003945	0.001309	0.002142	0.005277	0.002834	0.002248	0.004574	0.003576
31	0.001755	0.003664	0.001267	0.002571	0.004950	0.002695	0.002318	0.004897	0.002940
32	0.002168	0.004492	0.001893	0.002473	0.005806	0.002744	0.002178	0.005159	0.003183
33	0.001980	0.003528	0.001819	0.003148	0.004346	0.004114	0.002493	0.004555	0.000999
34	0.002260	0.003058	0.002059	0.003084	0.003712	0.003160	0.001956	0.003780	0.000542
35	0.001778	0.003292	0.001702	0.002715	0.004674	0.002987	0.001906	0.004102	0.002241
36	0.001648	0.003179	0.001363	0.002412	0.004875	0.002898	0.001887	0.004423	0.002623
37	0.002054	0.003800	0.001320	0.002814	0.005712	0.003448	0.001921	0.005104	0.002983
38	0.002158	0.003339	0.001573	0.002582	0.004315	0.002750	0.002172	0.003937	0.000868
39	0.001692	0.004344	0.001334	0.001913	0.005428	0.002669	0.002426	0.005453	0.004063
40	0.001936	0.004435	0.001569	0.002409	0.006121	0.002651	0.002114	0.005239	0.004244
41	0.001336	0.003901	0.001450	0.002356	0.005833	0.002517	0.001917	0.005492	0.003471
42	0.001774	0.003641	0.001663	0.002849	0.004891	0.003550	0.002057	0.004204	0.001305
43	0.002138	0.004327	0.001552	0.003074	0.005200	0.003669	0.002472	0.004665	0.002564
44	0.002252	0.004131	0.001340	0.003162	0.005379	0.003570	0.002292	0.004549	0.002709

LITTLEM_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
22	0.001785	0.003750	0.001787	0.002236	0.005582	0.002740	0.002084	0.005468	0.004154
23	0.002356	0.004103	0.002114	0.002144	0.006150	0.002857	0.002435	0.005605	0.004182
24	0.001761	0.004529	0.001863	0.001663	0.006232	0.002777	0.002075	0.006388	0.005421
25	0.002469	0.003776	0.001694	0.002690	0.005523	0.003226	0.001929	0.005328	0.003356
26	0.001614	0.003995	0.001388	0.001859	0.005453	0.002806	0.002331	0.003904	0.003043
27	0.002439	0.003094	0.001652	0.002928	0.003759	0.003048	0.001786	0.003501	0.000319
28	0.002013	0.003729	0.001581	0.001976	0.005615	0.003668	0.001817	0.004648	0.002764
29	0.002069	0.003301	0.001672	0.002546	0.005001	0.003122	0.002194	0.003922	0.002122
30	0.002039	0.003404	0.001828	0.002421	0.004544	0.002933	0.002504	0.004006	0.001399
31	0.002119	0.003720	0.001908	0.002745	0.004934	0.003295	0.002135	0.004356	0.002103
32	0.002040	0.003910	0.001693	0.002290	0.005137	0.002189	0.002139	0.004326	0.003673
33	0.002157	0.003305	0.001678	0.002549	0.004532	0.002905	0.001950	0.003680	0.001372
34	0.001634	0.003516	0.001947	0.001825	0.005785	0.002827	0.002078	0.005221	0.003687
35	0.002629	0.002680	0.001933	0.002863	0.003304	0.003582	0.001653	0.003959	0.000297
36	0.001513	0.003794	0.001426	0.001936	0.005032	0.002308	0.002040	0.005612	0.004019
37	0.002288	0.002880	0.001789	0.002825	0.004383	0.002626	0.002053	0.003920	0.001238
38	0.001798	0.004185	0.001661	0.001980	0.006185	0.002649	0.002544	0.005207	0.004244
39	0.002939	0.003712	0.001797	0.002720	0.005412	0.003182	0.002344	0.004110	0.001847
40	0.002071	0.003683	0.002083	0.001927	0.005278	0.003588	0.001870	0.005416	0.003501
41	0.002872	0.003411	0.001898	0.002803	0.004575	0.003542	0.002123	0.004352	0.001253
42	0.001610	0.004117	0.001212	0.002317	0.006000	0.002830	0.002185	0.005162	0.004432

LITTLEW_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.002052	0.004010	0.001842	0.002318	0.005238	0.003585	0.002448	0.004331	0.002100
2	0.001776	0.003493	0.001483	0.002046	0.005644	0.002693	0.002659	0.004547	0.003660
3	0.001800	0.004667	0.001469	0.002537	0.006348	0.003205	0.002148	0.004726	0.003391
4	0.001973	0.003821	0.001636	0.002361	0.004632	0.003131	0.002273	0.004230	0.002058
5	0.001650	0.004220	0.002032	0.002279	0.006232	0.002956	0.002502	0.005562	0.003870
6	0.000982	0.004549	0.001082	0.002181	0.007809	0.002587	0.002988	0.005677	0.005554
7	0.002452	0.003284	0.002122	0.002751	0.004118	0.004315	0.001663	0.003842	0.000161
8	0.002709	0.003708	0.001602	0.003192	0.004442	0.003729	0.002076	0.004001	0.000252
9	0.001779	0.004138	0.001609	0.002076	0.005712	0.002866	0.001917	0.004763	0.002859
10	0.001418	0.003847	0.000874	0.001713	0.005573	0.002275	0.002343	0.005104	0.004079
11	0.001891	0.003679	0.001705	0.002106	0.005079	0.003160	0.002010	0.004042	0.001797
12	0.001988	0.003286	0.001776	0.002166	0.004791	0.002892	0.002145	0.004554	0.002576
13	0.001967	0.004359	0.001504	0.001937	0.006608	0.002617	0.002350	0.005404	0.004359
14	0.002228	0.004812	0.001838	0.002128	0.006566	0.002118	0.003234	0.005225	0.004485
15	0.002188	0.004199	0.001674	0.002536	0.005234	0.003718	0.002517	0.004935	0.001647
16	0.001674	0.004190	0.001407	0.002411	0.006027	0.003191	0.002104	0.005736	0.004183
17	0.002062	0.004028	0.002171	0.003645	0.003928	0.004915	0.001920	0.004517	-0.000418
18	0.002007	0.004173	0.001206	0.003007	0.005261	0.004329	0.002334	0.004976	0.001468
19	0.001799	0.003741	0.001494	0.002345	0.005568	0.003326	0.002198	0.004464	0.003070
20	0.001854	0.004408	0.001627	0.002018	0.006083	0.002836	0.001995	0.005205	0.003432
21	0.002394	0.003444	0.001923	0.003033	0.003756	0.003011	0.002050	0.003941	0.000181
22	0.001209	0.004786	0.001257	0.001498	0.007109	0.003047	0.002529	0.005054	0.004750
23	0.001546	0.004350	0.001629	0.002712	0.005536	0.002524	0.002708	0.005388	0.002995
24	0.001427	0.003717	0.001649	0.002259	0.005025	0.002576	0.002276	0.004938	0.002704
25	0.001326	0.003847	0.001048	0.002082	0.006085	0.002729	0.002182	0.005708	0.004720
26	0.001900	0.003772	0.000768	0.002118	0.004783	0.002462	0.002277	0.003790	0.002071

ROSE_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
23	0.001609	0.003660	0.001236	0.002305	0.005043	0.002370	0.001954	0.004940	0.003559
24	0.001425	0.004351	0.001625	0.002022	0.005578	0.002525	0.002311	0.005487	0.004037
25	0.001402	0.003752	0.001074	0.002127	0.005203	0.002545	0.002153	0.005468	0.003955
26	0.001662	0.004195	0.001591	0.002177	0.005248	0.002305	0.001908	0.005493	0.003854
27	0.001895	0.003833	0.001689	0.002083	0.004454	0.002666	0.002131	0.005202	0.003150
28	0.002000	0.003267	0.001573	0.002402	0.004061	0.003286	0.001835	0.004129	0.001605
29	0.001352	0.003378	0.001359	0.001965	0.005141	0.002507	0.002147	0.004419	0.003433
30	0.001730	0.003685	0.001645	0.002391	0.004637	0.002935	0.001753	0.004979	0.003192
31	0.001780	0.003626	0.001460	0.002412	0.004425	0.003060	0.001865	0.004883	0.002682
32	0.002553	0.003955	0.001511	0.003158	0.005027	0.003279	0.002102	0.005489	0.002702
33	0.001554	0.004090	0.001403	0.002253	0.005636	0.002911	0.002116	0.005364	0.003876
34	0.001690	0.003583	0.001409	0.002306	0.004720	0.002682	0.001946	0.004542	0.003165
35	0.001784	0.003729	0.001829	0.002146	0.005086	0.002592	0.002075	0.005526	0.003610
36	0.001960	0.004136	0.001869	0.002618	0.005566	0.002823	0.001825	0.005561	0.002617
37	0.002109	0.003172	0.001570	0.003495	0.003360	0.004140	0.001753	0.004614	-0.000605
38	0.002296	0.003639	0.001932	0.002444	0.005113	0.003207	0.002039	0.004680	0.002460
39	0.001638	0.004109	0.001571	0.002135	0.005512	0.002635	0.002359	0.005273	0.003764
40	0.001420	0.003727	0.001078	0.002186	0.005115	0.002679	0.002431	0.005137	0.003963
41	0.000929	0.004736	0.001014	0.001569	0.006064	0.002568	0.002164	0.006271	0.005930
42	0.001268	0.004454	0.001328	0.001808	0.006361	0.002807	0.002030	0.006014	0.005567
43	0.001835	0.003452	0.001559	0.002525	0.005189	0.002955	0.002071	0.004885	0.002908
44	0.001237	0.003915	0.000799	0.001604	0.005566	0.002238	0.001692	0.004857	0.004586

UNDER_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.002549	0.003411	0.001843	0.002711	0.004791	0.003506	0.002684	0.003989	0.000824
2	0.001793	0.003777	0.001297	0.002110	0.004738	0.003673	0.002419	0.004055	0.001927
3	0.002085	0.003429	0.001932	0.002826	0.005213	0.003500	0.001680	0.004076	0.001397
4	0.002407	0.003201	0.001971	0.002944	0.005131	0.002912	0.002636	0.003701	0.000866
5	0.001834	0.003775	0.002256	0.002640	0.005407	0.003162	0.002346	0.005033	0.002706
6	0.001181	0.004377	0.001346	0.001833	0.006532	0.002028	0.002331	0.006214	0.005582
7	0.001725	0.004087	0.001815	0.002544	0.006032	0.002877	0.003189	0.004218	0.002466
8	0.001874	0.005514	0.001895	0.003013	0.007739	0.002827	0.002836	0.006139	0.003768
9	0.001541	0.004073	0.001475	0.002695	0.006097	0.002523	0.002821	0.004585	0.002720
10	0.001626	0.003483	0.001842	0.002841	0.005638	0.003646	0.001955	0.004739	0.001700
11	0.001848	0.003506	0.001620	0.002880	0.005513	0.003190	0.001978	0.004723	0.003404
12	0.002026	0.003741	0.001569	0.002374	0.005405	0.002449	0.002516	0.004764	0.003607
13	0.002283	0.004214	0.001735	0.002904	0.004515	0.002886	0.002277	0.004066	0.000770
14	0.002335	0.003083	0.001739	0.002965	0.004040	0.003277	0.001998	0.003329	0.000226
15	0.002205	0.003178	0.001763	0.003835	0.004419	0.003586	0.001975	0.003186	-0.000245
16	0.002425	0.003254	0.001949	0.003223	0.003783	0.003416	0.001871	0.003570	0.000136
17	0.001985	0.003551	0.001333	0.002892	0.004604	0.002393	0.002198	0.004236	0.001973
18	0.001832	0.003106	0.001869	0.002669	0.004908	0.002518	0.001819	0.004344	0.001873
19	0.001917	0.003753	0.001735	0.002805	0.004928	0.002617	0.002633	0.004242	0.001597
20	0.001867	0.004530	0.001365	0.002210	0.005259	0.001976	0.002502	0.005107	0.002948
21	0.001794	0.004255	0.001628	0.002406	0.005464	0.002693	0.001979	0.004872	0.002501
22	0.001815	0.003719	0.001652	0.002582	0.004581	0.002464	0.002267	0.003927	0.001139
23	0.001584	0.003431	0.001447	0.002445	0.004878	0.002764	0.001788	0.004236	0.001508
24	0.001429	0.003329	0.001414	0.002441	0.004153	0.002344	0.001435	0.004432	0.002144

POE1_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.001730	0.002349	0.001298	0.002443	0.002332	0.002380	0.001464	0.003069	0.000164
2	0.001763	0.002934	0.001305	0.002483	0.002925	0.002309	0.001873	0.003522	0.001008
3	0.001839	0.002918	0.001342	0.002634	0.003720	0.002219	0.001589	0.004329	0.001976
4	0.001839	0.002068	0.001201	0.002619	0.002164	0.002357	0.001524	0.003019	0.000168
5	0.001741	0.002245	0.001140	0.002318	0.002318	0.002366	0.001316	0.003051	0.000477
6	0.001590	0.003068	0.000820	0.002482	0.003149	0.002126	0.001517	0.003783	0.001812
7	0.002317	0.002207	0.001517	0.003564	0.002163	0.003376	0.001531	0.002662	-0.001412
8	0.001619	0.002624	0.001497	0.001964	0.005094	0.003226	0.001986	0.003229	0.001505

POE2_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.001388	0.002631	0.001088	0.002046	0.002484	0.002055	0.001552	0.003861	0.001597
2	0.002267	0.002486	0.001931	0.003169	0.003162	0.003161	0.001604	0.003646	-0.000070
3	0.002452	0.002592	0.001714	0.003675	0.002274	0.003192	0.001818	0.002919	-0.001598
4	0.001483	0.002492	0.001204	0.002095	0.002214	0.002108	0.001336	0.004870	0.002183
5	0.001629	0.002746	0.001210	0.002903	0.002999	0.002738	0.001808	0.003624	0.000699
6	0.001686	0.002896	0.001435	0.003017	0.002694	0.003077	0.002093	0.003693	0.000121
7	0.002642	0.002188	0.001949	0.003538	0.002438	0.003559	0.001455	0.002584	-0.001417
8	0.002090	0.002141	0.001416	0.002706	0.002540	0.003079	0.001729	0.002636	-0.000483
9	0.003100	0.001197	0.002210	0.003699	0.001192	0.004640	0.001920	0.001011	-0.003636
10	0.002185	0.001567	0.001952	0.003267	0.002718	0.003863	0.001794	0.002026	-0.001455
11	0.001583	0.002524	0.001174	0.002613	0.003900	0.003127	0.001760	0.003685	0.000504
12	0.002522	0.002537	0.001712	0.003429	0.001849	0.003565	0.001023	0.002946	-0.002033
13	0.001430	0.003045	0.001412	0.002107	0.004087	0.002830	0.001389	0.003572	0.001982
14	0.001686	0.002782	0.001018	0.002588	0.003662	0.002918	0.002193	0.003112	0.000949
15	0.002360	0.002054	0.001357	0.003452	0.001714	0.002984	0.001424	0.002120	-0.001857
16	0.001644	0.002244	0.001365	0.002850	0.002075	0.002918	0.001308	0.002450	-0.000723
17	0.001382	0.002690	0.000768	0.001793	0.003760	0.001845	0.001642	0.003700	0.002387
18	0.001282	0.003333	0.001298	0.001375	0.004990	0.002427	0.002443	0.004411	0.003500
19	0.001958	0.001911	0.001321	0.002425	0.002275	0.002421	0.001306	0.002646	0.000112
20	0.003535	0.002295	0.002355	0.005030	0.002169	0.004819	0.001562	0.002166	-0.004007
21	0.002560	0.002213	0.001570	0.003697	0.002841	0.003883	0.001974	0.002917	-0.001081
22	0.001743	0.003543	0.001379	0.002482	0.004521	0.002648	0.001645	0.004014	0.001896

POE3_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.001852	0.002303	0.001319	0.002805	0.002827	0.003004	0.001415	0.002615	-0.000300
2	0.001975	0.002989	0.001447	0.003145	0.004757	0.004224	0.002329	0.003130	0.000001
3	0.001914	0.002547	0.001507	0.002801	0.002530	0.002885	0.001959	0.003094	-0.000297
4	0.001509	0.002628	0.001037	0.001854	0.003519	0.002299	0.001687	0.003985	0.002253
5	0.002038	0.002396	0.001962	0.002830	0.002509	0.002935	0.001661	0.003364	-0.000038
6	0.001547	0.002564	0.001689	0.002270	0.003754	0.001992	0.001989	0.004907	0.001555

POE4_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.002208	0.003418	0.001410	0.003039	0.003556	0.002442	0.001314	0.005215	0.002064
2	0.000865	0.003475	0.001212	0.002392	0.005598	0.002030	0.001609	0.007227	0.005574
3	0.001727	0.002297	0.002106	0.002446	0.002505	0.002571	0.001227	0.003849	-0.000156
4	0.001982	0.002217	0.001241	0.002844	0.002337	0.002499	0.001560	0.002583	-0.000344
5	0.001518	0.002305	0.001212	0.002454	0.003084	0.002566	0.001415	0.004010	0.001432
6	0.001330	0.002634	0.001068	0.001784	0.003284	0.002368	0.001607	0.004015	0.001981
7	0.001639	0.003248	0.001035	0.001972	0.003389	0.001986	0.001742	0.005709	0.003164
8	0.001819	0.002460	0.001420	0.002429	0.003257	0.002238	0.001543	0.004152	0.001907
9	0.001762	0.002941	0.001280	0.002121	0.003980	0.002283	0.002422	0.004358	0.003011
10	0.001358	0.003343	0.001153	0.001621	0.003730	0.001576	0.001632	0.005172	0.002519
11	0.001521	0.002147	0.001226	0.002195	0.002406	0.002210	0.001243	0.003279	0.000617
12	0.001584	0.002918	0.001313	0.002048	0.003590	0.002945	0.001990	0.003947	0.001238
13	0.001648	0.002436	0.001796	0.002723	0.002200	0.002976	0.001408	0.003305	0.000147
14	0.001454	0.003406	0.001236	0.002336	0.003505	0.002277	0.001970	0.004709	0.002112
15	0.001620	0.003092	0.001566	0.001910	0.003646	0.002304	0.001573	0.004992	0.002219
16	0.001584	0.002868	0.000861	0.001458	0.003888	0.001534	0.001735	0.003855	0.002920
17	0.001224	0.002298	0.001046	0.001766	0.002186	0.002032	0.001393	0.003679	0.001060
18	0.001856	0.002596	0.001302	0.002624	0.003173	0.002814	0.001481	0.003273	0.000777
19	0.001980	0.002003	0.001909	0.003483	0.002360	0.003052	0.001440	0.003090	-0.001007

POE5_chaps.style.background_gradient(cmap='YlGn', high=.25)

	anger	anticipation	disgust	fear	joy	sadness	surprise	trust	polarity
chap_num
1	0.001793	0.002454	0.001667	0.001775	0.003737	0.002615	0.001428	0.003914	0.002327
2	0.001835	0.003057	0.001478	0.002568	0.003499	0.002888	0.002000	0.004059	0.001609
3	0.001870	0.002206	0.001632	0.003402	0.002952	0.003448	0.001663	0.002932	-0.000437
4	0.001588	0.002672	0.001020	0.002272	0.003879	0.002332	0.002117	0.003444	0.001659
5	0.002313	0.002639	0.001337	0.003201	0.003107	0.002827	0.002053	0.002695	-0.000078
6	0.002083	0.002543	0.001783	0.002507	0.002525	0.002946	0.001216	0.003876	0.000195
7	0.001999	0.002211	0.001417	0.002223	0.002475	0.002614	0.001735	0.004142	0.001043
8	0.002384	0.003240	0.001089	0.002932	0.005959	0.003410	0.002626	0.005965	0.003344
9	0.001637	0.002653	0.001472	0.001957	0.002769	0.002323	0.001608	0.004346	0.002026
10	0.001352	0.002556	0.000923	0.002080	0.003154	0.002255	0.001782	0.004080	0.001826

Create Stacked Dataframes (for Plotly Express)

EIGHT_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(eight))
JACKJILL_chaps_thin= EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(jackjill))
OLD_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(old))
JO_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(jo))
LITTLEM_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(littleM))
LITTLEW_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(littleW))
ROSE_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(rose))
UNDER_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(under))
POE1_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(poe1))
POE2_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(poe2))
POE3_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(poe3))
POE4_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(poe4))
POE5_chaps_thin = EMO_CHAPS.stack().to_frame().reset_index().rename(columns={0:'value','level_2':'emo'}).query("book_id == {}".format(poe5))

def plot_sentiments(df, emo='polarity'):
    FIG = dict(figsize=(25, 5), legend=True, fontsize=14, rot=45)
    df[emo].plot(**FIG)

# plot_sentiments(PERSUASION_chaps, emo_cols)
px.line(EIGHT_chaps_thin, x='chap_num', y='value', color='emo')

# plot_sentiments(MOBYDICK_chaps, emo_cols)
px.line(JACKJILL_chaps_thin, x='chap_num', y='value', color='emo')

px.line(OLD_chaps_thin, x='chap_num', y='value', color='emo')

px.line(JO_chaps_thin, x='chap_num', y='value', color='emo')

px.line(LITTLEM_chaps_thin, x='chap_num', y='value', color='emo')

px.line(LITTLEW_chaps_thin, x='chap_num', y='value', color='emo')

px.line(ROSE_chaps_thin, x='chap_num', y='value', color='emo')

px.line(UNDER_chaps_thin, x='chap_num', y='value', color='emo')

px.line(POE1_chaps_thin, x='chap_num', y='value', color='emo')

px.line(POE2_chaps_thin, x='chap_num', y='value', color='emo')

px.line(POE3_chaps_thin, x='chap_num', y='value', color='emo')

px.line(POE4_chaps_thin, x='chap_num', y='value', color='emo')

px.line(POE5_chaps_thin, x='chap_num', y='value', color='emo')

Cose Read Sentiment in Texts

Combine VOCAB + SALEX with TOKENS

We need to do this to reconstruct the sentences, which are lost in the BOW representation.

TOKENSIDX = TOKENS.index
T = TOKENS.merge(V, on='term_str', how='left')
T.index = TOKENSIDX
T = T.fillna(0)

EIGHT2 = T.loc[eight].copy()
JACKJILL2 = T.loc[jackjill].copy()
OLD2 = T.loc[old].copy()
JO2 = T.loc[jo].copy()
LITTLEM2 = T.loc[littleM].copy()
LITTLEW2 = T.loc[littleW].copy()
ROSE2 = T.loc[rose].copy()
UNDER2 = T.loc[under].copy()
POE12 = T.loc[poe1].copy()
POE22 = T.loc[poe2].copy() 
POE32 = T.loc[poe3].copy()
POE42 = T.loc[poe4].copy()
POE52 = T.loc[poe5].copy()

emo = 'polarity'

EIGHT2['html'] = EIGHT2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
JACKJILL2['html'] =  JACKJILL2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
OLD2['html'] =  OLD2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
JO2['html'] =  JO2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
LITTLEM2['html'] =  LITTLEM2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
LITTLEW2['html'] =  LITTLEW2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
ROSE2['html'] =  ROSE2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
UNDER2['html'] =  UNDER2.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
POE12['html'] =  POE12.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
POE22['html'] =  POE22.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
POE32['html'] =  POE32.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
POE42['html'] =  POE42.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)
POE52['html'] =  POE52.apply(lambda x: "<span class='sent{}'>{}</span>".format(int(np.sign(x[emo])), x.term_str), 1)

EIGHT2['html'].sample(10)

chap_num  para_num  sent_num  token_num
4         58        0         10             <span class='sent0'>declare</span>
21        20        1         15                 <span class='sent0'>and</span>
17        83        1         7                <span class='sent0'>tarts</span>
          62        3         10                  <span class='sent0'>to</span>
18        26        1         23                  <span class='sent0'>of</span>
7         77        2         25                   <span class='sent0'>a</span>
4         39        0         4                  <span class='sent0'>the</span>
2         2         1         2                <span class='sent0'>could</span>
24        96        3         8            <span class='sent0'>gutenberg</span>
9         93        0         9                  <span class='sent0'>who</span>
Name: html, dtype: object

EIGHT2

				pos_tuple	pos	token_str	term_str	term_id	term_rank	n	num	stop	stem_porter	...	disgust	fear	joy	negative	positive	sadness	surprise	trust	polarity	html
chap_num	para_num	sent_num	token_num
1	0	0	0	('Rose', 'NNP')	NNP	Rose	rose	22269	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>rose</span>
			1	('sat', 'VBD')	VBD	sat	sat	22651	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>sat</span>
			2	('all', 'DT')	DT	all	all	889	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>all</span>
			3	('alone', 'RB')	RB	alone	alone	951	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>alone</span>
			4	('in', 'IN')	IN	in	in	13335	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>in</span>
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
24	129	0	40	('to', 'TO')	TO	to	to	26510	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>to</span>
			41	('hear', 'VB')	VB	hear	hear	12264	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>hear</span>
			42	('about', 'IN')	IN	about	about	283	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>about</span>
			43	('new', 'JJ')	JJ	new	new	17347	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>new</span>
			44	('eBooks', 'NNS')	NNS	eBooks	ebooks	8403	0.0	0.0	0.0	0.0	0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	<span class='sent0'>ebooks</span>

77648 rows × 35 columns

EIGHT2_sents = EIGHT2.groupby(SENTS[1:])[emo_cols].mean()
JACKJILL2_sents = JACKJILL2.groupby(SENTS[1:])[emo_cols].mean()
OLD2_sents = OLD2.groupby(SENTS[1:])[emo_cols].mean()
JO2_sents = JO2.groupby(SENTS[1:])[emo_cols].mean()
LITTLEM2_sents = LITTLEM2.groupby(SENTS[1:])[emo_cols].mean()
LITTLEW2_sents = LITTLEW2.groupby(SENTS[1:])[emo_cols].mean()
ROSE2_sents = ROSE2.groupby(SENTS[1:])[emo_cols].mean()
UNDER2_sents = UNDER2.groupby(SENTS[1:])[emo_cols].mean()
POE12_sents = POE12.groupby(SENTS[1:])[emo_cols].mean()
POE22_sents = POE22.groupby(SENTS[1:])[emo_cols].mean()
POE32_sents = POE32.groupby(SENTS[1:])[emo_cols].mean()
POE42_sents = POE42.groupby(SENTS[1:])[emo_cols].mean()
POE52_sents = POE52.groupby(SENTS[1:])[emo_cols].mean()

EIGHT2_sents['sent_str'] = EIGHT2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
EIGHT2['html_str'] = EIGHT2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

JACKJILL2_sents['sent_str'] = JACKJILL2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
JACKJILL2_sents['html_str'] = JACKJILL2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

OLD2_sents['sent_str'] = OLD2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
OLD2_sents['html_str'] = OLD2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

JO2_sents['sent_str'] = JO2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
JO2_sents['html_str'] = JO2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

LITTLEM2_sents['sent_str'] = LITTLEM2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
LITTLEM2_sents['html_str'] = LITTLEM2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

LITTLEW2_sents['sent_str'] = LITTLEW2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
LITTLEW2_sents['html_str'] = LITTLEW2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

ROSE2_sents['sent_str'] = ROSE2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
ROSE2_sents['html_str'] = ROSE2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

UNDER2_sents['sent_str'] = UNDER2.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
UNDER2_sents['html_str'] = UNDER2.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

POE12_sents['sent_str'] = POE12.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
POE12_sents['html_str'] = POE12.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

POE22_sents['sent_str'] = POE22.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
POE22_sents['html_str'] = POE22.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

POE32_sents['sent_str'] = POE32.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
POE32_sents['html_str'] = POE32.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

POE42_sents['sent_str'] = POE42.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
POE42_sents['html_str'] = POE42.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

POE52_sents['sent_str'] = POE52.groupby(SENTS[1:]).term_str.apply(lambda x: x.str.cat(sep=' '))
POE52_sents['html_str'] = POE52.groupby(SENTS[1:]).html.apply(lambda x: x.str.cat(sep=' '))

def sample_sentences(df):
    rows = []
    for idx in df.sample(10).index:

        valence = round(df.loc[idx, emo], 4)     
        t = 0
        if valence > t: color = '#ccffcc'
        elif valence < t: color = '#ffcccc'
        else: color = '#f2f2f2'
        z=0
        rows.append("""<tr style="background-color:{0};padding:.5rem 1rem;font-size:110%;">
        <td>{1}</td><td>{3}</td><td width="400" style="text-align:left;">{2}</td>
        </tr>""".format(color, valence, df.loc[idx, 'html_str'], idx))

    display(HTML('<style>#sample1 td{font-size:120%;vertical-align:top;} .sent-1{color:red;font-weight:bold;} .sent1{color:green;font-weight:bold;}</style>'))
    display(HTML('<table id="sample1"><tr><th>Sentiment</th><th>ID</th><th width="600">Sentence</th></tr>'+''.join(rows)+'</table>'))

# sample_sentences(EIGHT2_sents)

# sample_sentences(JACKJILL2_sents)

Try VADER

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()

EIGHT_vader_cols = EIGHT2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
EIGHT_vader = pd.concat([EIGHT2_sents, EIGHT_vader_cols], axis=1)

w = int(EIGHT_vader.shape[0] / 5)
EIGHT_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
EIGHT_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
EIGHT_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477dcb17f0>

JACKJILL_vader_cols = JACKJILL2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
JACKJILL_vader = pd.concat([JACKJILL2_sents, JACKJILL_vader_cols], axis=1)

w = int(JACKJILL_vader.shape[0] / 5)
JACKJILL_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
JACKJILL_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
JACKJILL_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2470f09b048>

OLD_vader_cols = OLD2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
OLD_vader = pd.concat([OLD2_sents, OLD_vader_cols], axis=1)
w = int(OLD_vader.shape[0] / 5)
OLD_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
OLD_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
OLD_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477e524898>

JO_vader_cols = JO2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
JO_vader = pd.concat([JO2_sents, JO_vader_cols], axis=1)
w = int(JO_vader.shape[0] / 5)
JO_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
JO_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
JO_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477e155080>

LITTLEM_vader_cols = LITTLEM2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
LITTLEM_vader = pd.concat([LITTLEM2_sents, LITTLEM_vader_cols], axis=1)
w = int(LITTLEM_vader.shape[0] / 5)
LITTLEM_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
LITTLEM_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
LITTLEM_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x247016acf98>

LITTLEW_vader_cols = LITTLEW2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
LITTLEW_vader = pd.concat([LITTLEW2_sents, LITTLEW_vader_cols], axis=1)
w = int(LITTLEW_vader.shape[0] / 5)
LITTLEW_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
LITTLEW_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
LITTLEW_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x24704fa0fd0>

ROSE_vader_cols = ROSE2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
ROSE_vader = pd.concat([ROSE2_sents, ROSE_vader_cols], axis=1)
w = int(ROSE_vader.shape[0] / 5)
ROSE_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
ROSE_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
ROSE_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2470fa18748>

UNDER_vader_cols = UNDER2_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
UNDER_vader = pd.concat([UNDER2_sents, UNDER_vader_cols], axis=1)
w = int(UNDER_vader.shape[0] / 5)
UNDER_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
UNDER_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
UNDER_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477d7847b8>

POE1_vader_cols = POE12_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
POE1_vader = pd.concat([POE12_sents, POE1_vader_cols], axis=1)
w = int(POE1_vader.shape[0] / 5)
POE1_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
POE1_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
POE1_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477e5052b0>

POE2_vader_cols = POE22_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
POE2_vader = pd.concat([POE22_sents, POE2_vader_cols], axis=1)
w = int(POE2_vader.shape[0] / 5)
POE2_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
POE2_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
POE2_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477dca2ac8>

POE3_vader_cols = POE32_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
POE3_vader = pd.concat([POE32_sents, POE3_vader_cols], axis=1)
w = int(POE3_vader.shape[0] / 5)
POE3_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
POE3_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
POE3_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477e6e1358>

POE4_vader_cols = POE42_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
POE4_vader = pd.concat([POE42_sents, POE4_vader_cols], axis=1)
w = int(POE4_vader.shape[0] / 5)
POE4_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
POE4_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
POE4_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477e522c50>

POE5_vader_cols = POE52_sents.sent_str.apply(analyser.polarity_scores).apply(lambda x: pd.Series(x))
POE5_vader = pd.concat([POE52_sents, POE5_vader_cols], axis=1)
w = int(POE5_vader.shape[0] / 5)
POE5_vader[['pos','neg']].rolling(w).mean().plot(figsize=(25,5))
POE5_vader[['neu']].rolling(w).mean().plot(figsize=(25,5))
POE5_vader[['compound']].rolling(w).mean().plot(figsize=(25,5))

<matplotlib.axes._subplots.AxesSubplot at 0x2477d275828>